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SUN 6 DEC
5 a.m.
Expo Workshop:
Machine Learning at Netflix
(ends 8:00 AM)
Expo Workshop:
Building AI with Security and Privacy in mind
(ends 9:00 AM)
Expo Talk Panel:
scikit-learn and fairness, tools and challenges
(ends 6:00 AM)
Expo Talk Panel:
The challenges and latest advances in the field of causal AI
(ends 6:00 AM)
6 a.m.
Expo Talk Panel:
How we leverage machine learning and AI to develop life-changing medicines - a case study with COVID-19.
(ends 7:00 AM)
7 a.m.
Expo Talk Panel:
Accelerated Training with ML Compute on M1-Powered Macs
(ends 8:00 AM)
Expo Talk Panel:
Drifting Efficiently Through the Stratosphere Using Deep Reinforcement Learning
(ends 8:00 AM)
8 a.m.
Expo Talk Panel:
Making boats fly by scaling Reinforcement Learning with Software 2.0
(ends 9:00 AM)
9 a.m.
Expo Talk Panel:
Automating Wildlife Conservation for Cetaceans
(ends 10:00 AM)
Expo Talk Panel:
AI against COVID-19 at IBM Research
(ends 10:00 AM)
10 a.m.
Expo Workshop:
Real World RL with Vowpal Wabbit: Beyond Contextual Bandits
(ends 2:00 PM)
Expo Workshop:
Mining and Learning with Graphs at Scale
(ends 2:05 PM)
Expo Workshop:
Perspectives on Neurosymbolic Artificial Intelligence Research
(ends 1:55 PM)
Expo Talk Panel:
Fairness, Explainability, and Privacy in AI/ML Systems
(ends 11:00 AM)
11 a.m.
Expo Talk Panel:
AI4Code @ IBM and Red Hat
(ends 12:00 PM)
Expo Talk Panel:
Challenges in the adoption of Machine Learning in Health Care
(ends 12:00 PM)
noon
Expo Demonstration:
Using Sparse Quantization for Efficient Inference on Deep Neural Networks
(ends 1:00 PM)
1 p.m.
Expo Talk Panel:
Human-Centered AI @ IBM Research –Automation versus Collaboration in the Age of AI
(ends 2:00 PM)
Expo Talk Panel:
Modern ML Meets Financial Markets: Insights and Challenges
(ends 2:00 PM)
2 p.m.
Expo Talk Panel:
Building Neural Interfaces: When Real and Artificial Neurons Meet
(ends 3:00 PM)
Expo Talk Panel:
The Unpaved Path of Deploying Reliable and Human-Centered Machine Learning Systems
(ends 3:00 PM)
3 p.m.
Expo Workshop:
DAQA – Domain Adaptation and Question Answering
(ends 9:00 PM)
Expo Demonstration:
Beyond AutoML: AI Automation & Scaling
(ends 4:00 PM)
4 p.m.
Expo Talk Panel:
Scaling Data Labeling with Machine Learning
(ends 5:00 PM)
Expo Demonstration:
Medical Transcription Analysis
(ends 5:00 PM)
5 p.m.
Expo Talk Panel:
Hypotheses Generation for Applications in Biomedicine and Gastronomy
(ends 6:00 PM)
Expo Demonstration:
AWS Computer Vision Science
(ends 6:00 PM)
6 p.m.
Expo Talk Panel:
Visually Debugging ML Models With Scale Nucleus
(ends 7:00 PM)
Expo Demonstration:
Whale: Accelerate EasyTransfer training workloads within one unified distributed training framework
(ends 7:00 PM)
7 p.m.
Expo Talk Panel:
Driving New Frontiers of Machine Learning with Cruise
(ends 8:00 PM)
Expo Demonstration:
Accelerating Deep Learning for Entertainment with Sony's Neural Network Libraries and Console
(ends 8:00 PM)
8 p.m.
Expo Workshop:
New Challenges in User-Generated Content
(ends 11:59 PM)
Expo Workshop:
MachineLearningforAll-InclusiveFinance
(ends 12:00 AM)
Expo Talk Panel:
Accelerating Eye Movement Research Via Smartphone Gaze
(ends 9:00 PM)
Expo Demonstration:
The Intelligent Vision Sensor
(ends 9:00 PM)
9 p.m.
Expo Demonstration:
Discovering genetic medicines using the Deep Genomics AI Drug Discovery Platform
(ends 10:00 PM)
MON 7 DEC
midnight
Tutorial:
(Track1) Sketching and Streaming Algorithms
(ends 2:30 AM)
Tutorial:
(Track2) Deeper Conversational AI
(ends 2:30 AM)
Expo Demonstration:
GAN Applications in Fashion Article Design and Outfit Rendering
(ends 1:00 AM)
2:30 a.m.
Tutorial:
(Track1) There and Back Again: A Tale of Slopes and Expectations
(ends 5:00 AM)
Tutorial:
(Track2) Equivariant Networks
(ends 5:00 AM)
Tutorial:
(Track3) Designing Learning Dynamics
(ends 5:00 AM)
3 a.m.
Affinity Workshop:
New In ML
(ends 1:15 PM)
5:30 a.m.
Tutorial:
(Track1) Where Neuroscience meets AI (And What’s in Store for the Future)
(ends 8:00 AM)
Tutorial:
(Track2) Beyond Accuracy: Grounding Evaluation Metrics for Human-Machine Learning Systems
(ends 8:00 AM)
6 a.m.
Affinity Workshop:
Black in AI
(ends 12:30 PM)
8 a.m.
Tutorial:
(Track3) Offline Reinforcement Learning: From Algorithm Design to Practical Applications
(ends 10:30 AM)
Tutorial:
(Track2) Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning
(ends 10:30 AM)
Tutorial:
(Track1) Advances in Approximate Inference
(ends 10:30 AM)
Affinity Workshop:
LXAI Research @ NeurIPS 2020
(ends 7:00 PM)
11 a.m.
Tutorial:
(Track1) Abstraction & Reasoning in AI systems: Modern Perspectives
(ends 1:30 PM)
Tutorial:
(Track2) Machine Learning for Astrophysics and Astrophysics Problems for Machine Learning
(ends 1:30 PM)
Tutorial:
(Track3) Policy Optimization in Reinforcement Learning
(ends 1:30 PM)
12:30 p.m.
Affinity Poster Session:
Joint Affinity Groups Poster Session
(ends 2:30 PM)
1:30 p.m.
Tutorial:
(Track2) Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities
(ends 4:00 PM)
Tutorial:
(Track3) Deep Implicit Layers: Neural ODEs, Equilibrium Models, and Differentiable Optimization
(ends 4:00 PM)
Tutorial:
(Track1) Federated Learning and Analytics: Industry Meets Academia
(ends 4:00 PM)
5 p.m.
Invited Talk:
You Can’t Escape Hyperparameters and Latent Variables: Machine Learning as a Software Engineering Enterprise
Charles Isbell
(ends 7:00 PM)
6 p.m.
Orals & Spotlights Track 01: Representation/Relational [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Learning Physical Graph Representations from Visual Scenes
[6:15] Multi-label Contrastive Predictive Coding
[6:30] Equivariant Networks for Hierarchical Structures
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] On the Equivalence between Online and Private Learnability beyond Binary Classification
[7:10] Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
[7:20] Joint Contrastive Learning with Infinite Possibilities
[7:30] Neural Methods for Point-wise Dependency Estimation
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Design Space for Graph Neural Networks
[8:00] Debiased Contrastive Learning
[8:10] The Autoencoding Variational Autoencoder
[8:20] Unsupervised Representation Learning by Invariance Propagation
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 PM)
Orals & Spotlights Track 02: COVID/Health/Bio Applications [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
[6:15] Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement
[6:30] Neural encoding with visual attention
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations
[7:10] Using noise to probe recurrent neural network structure and prune synapses
[7:20] Interpretable Sequence Learning for Covid-19 Forecasting
[7:30] Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:20
[7:50] Demixed shared component analysis of neural population data from multiple brain areas
[8:00] Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects
[8:10] The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models
Q&A s 8:20-8:30
[8:20] Joint Q&A for Preceeding Spotlights
Break s 8:30-9:00
Break
(ends 9:00 PM)
Orals & Spotlights Track 03: Language/Audio Applications [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Language Models are Few-Shot Learners
[6:15] Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search
[6:30] The Cone of Silence: Speech Separation by Localization
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Unsupervised Sound Separation Using Mixture Invariant Training
[7:10] Investigating Gender Bias in Language Models Using Causal Mediation Analysis
[7:20] A Simple Language Model for Task-Oriented Dialogue
[7:30] ConvBERT: Improving BERT with Span-based Dynamic Convolution
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Cross-lingual Retrieval for Iterative Self-Supervised Training
[8:00] DynaBERT: Dynamic BERT with Adaptive Width and Depth
[8:10] Incorporating Pragmatic Reasoning Communication into Emergent Language
[8:20] De-Anonymizing Text by Fingerprinting Language Generation
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 PM)
Orals & Spotlights Track 04: Reinforcement Learning [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search
[6:15] Novelty Search in Representational Space for Sample Efficient Exploration
[6:30] Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] First Order Constrained Optimization in Policy Space
[7:10] CoinDICE: Off-Policy Confidence Interval Estimation
[7:20] DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction
[7:30] Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning
[8:00] Bayesian Multi-type Mean Field Multi-agent Imitation Learning
[8:10] Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity
[8:20] Safe Reinforcement Learning via Curriculum Induction
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 PM)
9 p.m.
Poster Session 1 [9:00-11:00]
Rankmax: An Adaptive Projection Alternative to the Softmax Function
Field-wise Learning for Multi-field Categorical Data
Deep Diffusion-Invariant Wasserstein Distributional Classification
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
Efficient Clustering Based On A Unified View Of K-means And Ratio-cut
Probabilistic Fair Clustering
Sampling-Decomposable Generative Adversarial Recommender
Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering
Ratio Trace Formulation of Wasserstein Discriminant Analysis
Coresets for Regressions with Panel Data
Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery
Adversarial Learning for Robust Deep Clustering
Adversarial Counterfactual Learning and Evaluation for Recommender System
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion
Interventional Few-Shot Learning
Neural Methods for Point-wise Dependency Estimation
Multi-label Contrastive Predictive Coding
Self-Supervised Relationship Probing
Debiased Contrastive Learning
The Autoencoding Variational Autoencoder
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction
Unsupervised Data Augmentation for Consistency Training
Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation
Hard Negative Mixing for Contrastive Learning
Parametric Instance Classification for Unsupervised Visual Feature learning
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain
Unsupervised Representation Learning by Invariance Propagation
Joint Contrastive Learning with Infinite Possibilities
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
Restoring Negative Information in Few-Shot Object Detection
One-sample Guided Object Representation Disassembling
Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning
Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation
Hierarchical Poset Decoding for Compositional Generalization in Language
Strongly Incremental Constituency Parsing with Graph Neural Networks
A Simple Language Model for Task-Oriented Dialogue
Learning Dynamic Belief Graphs to Generalize on Text-Based Games
Incorporating Pragmatic Reasoning Communication into Emergent Language
Learning Strategic Network Emergence Games
Fighting Copycat Agents in Behavioral Cloning from Observation Histories
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation
Can the Brain Do Backpropagation? --- Exact Implementation of Backpropagation in Predictive Coding Networks
Demixed shared component analysis of neural population data from multiple brain areas
Neural encoding with visual attention
On Numerosity of Deep Neural Networks
Compact task representations as a normative model for higher-order brain activity
Using noise to probe recurrent neural network structure and prune synapses
An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch
Language Models are Few-Shot Learners
Incorporating BERT into Parallel Sequence Decoding with Adapters
CogLTX: Applying BERT to Long Texts
MPNet: Masked and Permuted Pre-training for Language Understanding
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
Towards Neural Programming Interfaces
Language Through a Prism: A Spectral Approach for Multiscale Language Representations
ColdGANs: Taming Language GANs with Cautious Sampling Strategies
ConvBERT: Improving BERT with Span-based Dynamic Convolution
Investigating Gender Bias in Language Models Using Causal Mediation Analysis
Cross-lingual Retrieval for Iterative Self-Supervised Training
DynaBERT: Dynamic BERT with Adaptive Width and Depth
Pre-training via Paraphrasing
Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
Big Bird: Transformers for Longer Sequences
Self-Supervised Generative Adversarial Compression
The Generalized Lasso with Nonlinear Observations and Generative Priors
Robust compressed sensing using generative models
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher
Fourier Spectrum Discrepancies in Deep Network Generated Images
Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects
Towards a Combinatorial Characterization of Bounded-Memory Learning
The Power of Comparisons for Actively Learning Linear Classifiers
Estimating decision tree learnability with polylogarithmic sample complexity
Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach
Matrix Inference and Estimation in Multi-Layer Models
Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks
Limits on Testing Structural Changes in Ising Models
A Robust Functional EM Algorithm for Incomplete Panel Count Data
Weston-Watkins Hinge Loss and Ordered Partitions
On the Equivalence between Online and Private Learnability beyond Binary Classification
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods
Reinforcement Learning for Control with Multiple Frequencies
Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning
First Order Constrained Optimization in Policy Space
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction
Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes
MOPO: Model-based Offline Policy Optimization
Trust the Model When It Is Confident: Masked Model-based Actor-Critic
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity
MOReL: Model-Based Offline Reinforcement Learning
Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis
Independent Policy Gradient Methods for Competitive Reinforcement Learning
Provably Good Batch Reinforcement Learning Without Great Exploration
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks
Contrastive Learning with Adversarial Examples
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search
The Cone of Silence: Speech Separation by Localization
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
Swapping Autoencoder for Deep Image Manipulation
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond
Domain Generalization via Entropy Regularization
Self-Supervised Few-Shot Learning on Point Clouds
DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation
CLEARER: Multi-Scale Neural Architecture Search for Image Restoration
Language and Visual Entity Relationship Graph for Agent Navigation
Fast Fourier Convolution
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations
The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
Reinforced Molecular Optimization with Neighborhood-Controlled Grammars
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement
MRI Banding Removal via Adversarial Training
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction
Interpretable Sequence Learning for Covid-19 Forecasting
Adversarial Attacks on Deep Graph Matching
Adversarial Sparse Transformer for Time Series Forecasting
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks
Input-Aware Dynamic Backdoor Attack
Understanding Global Feature Contributions With Additive Importance Measures
Improving Policy-Constrained Kidney Exchange via Pre-Screening
Learning Black-Box Attackers with Transferable Priors and Query Feedback
De-Anonymizing Text by Fingerprinting Language Generation
Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
Design Space for Graph Neural Networks
Learning Physical Graph Representations from Visual Scenes
Equivariant Networks for Hierarchical Structures
ARMA Nets: Expanding Receptive Field for Dense Prediction
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning
How to Characterize The Landscape of Overparameterized Convolutional Neural Networks
Towards Learning Convolutions from Scratch
Bayesian Attention Modules
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
Understanding and Exploring the Network with Stochastic Architectures
Neuron Merging: Compensating for Pruned Neurons
Position-based Scaled Gradient for Model Quantization and Pruning
ShiftAddNet: A Hardware-Inspired Deep Network
Pruning neural networks without any data by iteratively conserving synaptic flow
Attribution Preservation in Network Compression for Reliable Network Interpretation
Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs
Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals
Cooperative Heterogeneous Deep Reinforcement Learning
The Mean-Squared Error of Double Q-Learning
Novelty Search in Representational Space for Sample Efficient Exploration
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis
Robust Multi-Agent Reinforcement Learning with Model Uncertainty
Bayesian Multi-type Mean Field Multi-agent Imitation Learning
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics
Safe Reinforcement Learning via Curriculum Induction
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction
Off-Policy Evaluation via the Regularized Lagrangian
Dynamic Regret of Policy Optimization in Non-Stationary Environments
Constrained episodic reinforcement learning in concave-convex and knapsack settings
Off-Policy Interval Estimation with Lipschitz Value Iteration
CoinDICE: Off-Policy Confidence Interval Estimation
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension
Task-agnostic Exploration in Reinforcement Learning
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss
Minimax Value Interval for Off-Policy Evaluation and Policy Optimization
Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems
The Power of Predictions in Online Control
Information Theoretic Regret Bounds for Online Nonlinear Control
(ends 11:00 PM)
TUE 8 DEC
2 a.m.
Tutorial:
(Track2) Equivariant Networks Q&A
(ends 2:50 AM)
3 a.m.
Tutorial:
(Track1) There and Back Again: A Tale of Slopes and Expectations Q&A
(ends 3:50 AM)
5 a.m.
Invited Talk:
Feedback Control Perspectives on Learning
Jeff Shamma
(ends 7:00 AM)
6 a.m.
Demonstrations 1 [6:00-9:20]
(duration 3.3 hr)
Orals & Spotlights Track 05: Clustering/Ranking [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Exact Recovery of Mangled Clusters with Same-Cluster Queries
[6:15] Deep Transformation-Invariant Clustering
[6:30] Partially View-aligned Clustering
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Simple and Scalable Sparse k-means Clustering via Feature Ranking
[7:10] Simultaneous Preference and Metric Learning from Paired Comparisons
[7:20] Learning Optimal Representations with the Decodable Information Bottleneck
[7:30] Manifold structure in graph embeddings
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:20
[7:50] Self-Supervised Learning by Cross-Modal Audio-Video Clustering
[8:00] Classification with Valid and Adaptive Coverage
[8:10] On ranking via sorting by estimated expected utility
Q&A s 8:20-8:30
[8:20] Joint Q&A for Preceeding Spotlights
Break s 8:30-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 06: Dynamical Sys/Density/Sparsity [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Deep Energy-based Modeling of Discrete-Time Physics
[6:15] SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory
[6:30] Dissecting Neural ODEs
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Robust Density Estimation under Besov IPM Losses
[7:10] Almost Surely Stable Deep Dynamics
[7:20] Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks
[7:30] A Theoretical Framework for Target Propagation
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Training Generative Adversarial Networks by Solving Ordinary Differential Equations
[8:00] Information theoretic limits of learning a sparse rule
[8:10] Constant-Expansion Suffices for Compressed Sensing with Generative Priors
[8:20] Logarithmic Pruning is All You Need
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 07: Vision Applications [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring
[6:15] Causal Intervention for Weakly-Supervised Semantic Segmentation
[6:30] Convolutional Generation of Textured 3D Meshes
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] DISK: Learning local features with policy gradient
[7:10] Wasserstein Distances for Stereo Disparity Estimation
[7:20] Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
[7:30] Learning Semantic-aware Normalization for Generative Adversarial Networks
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Neural Sparse Voxel Fields
[8:00] 3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data
[8:10] Learning to Detect Objects with a 1 Megapixel Event Camera
[8:20] A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 08: Deep Learning [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Multiscale Deep Equilibrium Models
[6:15] On the Modularity of Hypernetworks
[6:30] Training Generative Adversarial Networks with Limited Data
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] MeshSDF: Differentiable Iso-Surface Extraction
[7:10] GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
[7:20] Monotone operator equilibrium networks
[7:30] What Do Neural Networks Learn When Trained With Random Labels?
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks
[8:00] ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks
[8:10] The phase diagram of approximation rates for deep neural networks
[8:20] Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 09: Reinforcement Learning [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Improved Sample Complexity for Incremental Autonomous Exploration in MDPs
[6:15] Escaping the Gravitational Pull of Softmax
[6:30] FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Interferobot: aligning an optical interferometer by a reinforcement learning agent
[7:10] On Efficiency in Hierarchical Reinforcement Learning
[7:20] Finite-Time Analysis for Double Q-learning
[7:30] Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
[8:00] Model-based Policy Optimization with Unsupervised Model Adaptation
[8:10] Variational Policy Gradient Method for Reinforcement Learning with General Utilities
[8:20] Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 10: Social/Privacy [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Adversarially Robust Streaming Algorithms via Differential Privacy
[6:15] Differentially Private Clustering: Tight Approximation Ratios
[6:30] Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates
[7:10] Private Identity Testing for High-Dimensional Distributions
[7:20] Permute-and-Flip: A new mechanism for differentially private selection
[7:30] Smoothed Analysis of Online and Differentially Private Learning
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:20
[7:50] Optimal Private Median Estimation under Minimal Distributional Assumptions
[8:00] Assisted Learning: A Framework for Multi-Organization Learning
[8:10] Higher-Order Certification For Randomized Smoothing
Q&A s 8:20-8:30
[8:20] Joint Q&A for Preceeding Spotlights
Break s 8:30-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 11: Learning Theory [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
[6:15] Efficient active learning of sparse halfspaces with arbitrary bounded noise
[6:30] Learning Parities with Neural Networks
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] The Adaptive Complexity of Maximizing a Gross Substitutes Valuation
[7:10] Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics
[7:20] A Bandit Learning Algorithm and Applications to Auction Design
[7:30] An Optimal Elimination Algorithm for Learning a Best Arm
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:40
[7:50] Second Order PAC-Bayesian Bounds for the Weighted Majority Vote
[8:00] PAC-Bayesian Bound for the Conditional Value at Risk
[8:10] Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability
[8:20] Hedging in games: Faster convergence of external and swap regrets
[8:30] Online Bayesian Persuasion
Q&A s 8:40-8:50
[8:40] Joint Q&A for Preceeding Spotlights
Break s 8:50-9:00
Break
(ends 9:00 AM)
7 a.m.
Affinity Workshop:
Queer in AI Workshop @ NeurIPS 2020
(ends 12:30 PM)
7:30 a.m.
Affinity Workshop:
Muslims in ML
(ends 11:00 AM)
9 a.m.
Poster Session 2 [9:00-11:00]
Latent Template Induction with Gumbel-CRFs
Federated Principal Component Analysis
Learning Differential Equations that are Easy to Solve
Learning Rich Rankings
Self-supervised Co-Training for Video Representation Learning
Prophet Attention: Predicting Attention with Future Attention
Audeo: Audio Generation for a Silent Performance Video
Cascaded Text Generation with Markov Transformers
All Word Embeddings from One Embedding
Data Diversification: A Simple Strategy For Neural Machine Translation
Learning Sparse Prototypes for Text Generation
A Discrete Variational Recurrent Topic Model without the Reparametrization Trick
AViD Dataset: Anonymized Videos from Diverse Countries
Convolutional Tensor-Train LSTM for Spatio-Temporal Learning
End-to-End Learning and Intervention in Games
Cross-validation Confidence Intervals for Test Error
Learning Robust Decision Policies from Observational Data
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs
Self-Imitation Learning via Generalized Lower Bound Q-learning
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL
An operator view of policy gradient methods
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics
Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms
How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization
The Value Equivalence Principle for Model-Based Reinforcement Learning
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies
Neurosymbolic Reinforcement Learning with Formally Verified Exploration
Near-Optimal Reinforcement Learning with Self-Play
Variational Policy Gradient Method for Reinforcement Learning with General Utilities
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
Deep Multimodal Fusion by Channel Exchanging
Learning Representations from Audio-Visual Spatial Alignment
Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition
Causal Intervention for Weakly-Supervised Semantic Segmentation
Generative View Synthesis: From Single-view Semantics to Novel-view Images
Labelling unlabelled videos from scratch with multi-modal self-supervision
Unfolding the Alternating Optimization for Blind Super Resolution
Video Frame Interpolation without Temporal Priors
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation
Color Visual Illusions: A Statistics-based Computational Model
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection
Make One-Shot Video Object Segmentation Efficient Again
SIRI: Spatial Relation Induced Network For Spatial Description Resolution
Multi-Plane Program Induction with 3D Box Priors
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
Unsupervised object-centric video generation and decomposition in 3D
Dissecting Neural ODEs
On ranking via sorting by estimated expected utility
Constant-Expansion Suffices for Compressed Sensing with Generative Priors
Model Interpretability through the Lens of Computational Complexity
Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity
Smoothed Analysis of Online and Differentially Private Learning
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise
Hardness of Learning Neural Networks with Natural Weights
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability
The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise
The phase diagram of approximation rates for deep neural networks
A Dynamical Central Limit Theorem for Shallow Neural Networks
Learning Bounds for Risk-sensitive Learning
Agnostic Learning of a Single Neuron with Gradient Descent
Information theoretic limits of learning a sparse rule
From Finite to Countable-Armed Bandits
Optimal Best-arm Identification in Linear Bandits
Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits
Finite Continuum-Armed Bandits
Adversarial Blocking Bandits
Inference for Batched Bandits
Online Algorithm for Unsupervised Sequential Selection with Contextual Information
Adversarial Attacks on Linear Contextual Bandits
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality
Finding All $\epsilon$-Good Arms in Stochastic Bandits
An Optimal Elimination Algorithm for Learning a Best Arm
Instance-wise Feature Grouping
POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis
Online Planning with Lookahead Policies
Escaping the Gravitational Pull of Softmax
Online Bayesian Persuasion
KFC: A Scalable Approximation Algorithm for $k$−center Fair Clustering
CoinPress: Practical Private Mean and Covariance Estimation
Auditing Differentially Private Machine Learning: How Private is Private SGD?
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity
Smoothly Bounding User Contributions in Differential Privacy
Learning from Mixtures of Private and Public Populations
A Computational Separation between Private Learning and Online Learning
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms
Improving Sparse Vector Technique with Renyi Differential Privacy
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
Private Identity Testing for High-Dimensional Distributions
Optimal Private Median Estimation under Minimal Distributional Assumptions
Learning discrete distributions: user vs item-level privacy
Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
Phase retrieval in high dimensions: Statistical and computational phase transitions
Higher-Order Spectral Clustering of Directed Graphs
Deep Transformation-Invariant Clustering
Faster DBSCAN via subsampled similarity queries
From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering
Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering
Exact Recovery of Mangled Clusters with Same-Cluster Queries
Simple and Scalable Sparse k-means Clustering via Feature Ranking
Efficient Clustering for Stretched Mixtures: Landscape and Optimality
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian
Classification with Valid and Adaptive Coverage
Self-Supervised Learning by Cross-Modal Audio-Video Clustering
HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss
Distributionally Robust Local Non-parametric Conditional Estimation
Differentially Private Clustering: Tight Approximation Ratios
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC
Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes
Non-Euclidean Universal Approximation
WoodFisher: Efficient Second-Order Approximation for Neural Network Compression
Deep Transformers with Latent Depth
Movement Pruning: Adaptive Sparsity by Fine-Tuning
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot
Pruning Filter in Filter
The Lottery Ticket Hypothesis for Pre-trained BERT Networks
The Generalization-Stability Tradeoff In Neural Network Pruning
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks
HYDRA: Pruning Adversarially Robust Neural Networks
Logarithmic Pruning is All You Need
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient
Higher-Order Certification For Randomized Smoothing
Adversarial robustness via robust low rank representations
Denoised Smoothing: A Provable Defense for Pretrained Classifiers
Margins are Insufficient for Explaining Gradient Boosting
On the Power of Louvain in the Stochastic Block Model
Robust large-margin learning in hyperbolic space
Self-Learning Transformations for Improving Gaze and Head Redirection
Exactly Computing the Local Lipschitz Constant of ReLU Networks
Optimizing Mode Connectivity via Neuron Alignment
An Efficient Framework for Clustered Federated Learning
On the distance between two neural networks and the stability of learning
One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers
Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates
Consequences of Misaligned AI
Certified Defense to Image Transformations via Randomized Smoothing
Certifying Strategyproof Auction Networks
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
Improving robustness against common corruptions by covariate shift adaptation
Deeply Learned Spectral Total Variation Decomposition
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
Multiscale Deep Equilibrium Models
Faithful Embeddings for Knowledge Base Queries
GradAug: A New Regularization Method for Deep Neural Networks
Monotone operator equilibrium networks
Hierarchical nucleation in deep neural networks
What Do Neural Networks Learn When Trained With Random Labels?
H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks
Throughput-Optimal Topology Design for Cross-Silo Federated Learning
Wavelet Flow: Fast Training of High Resolution Normalizing Flows
Woodbury Transformations for Deep Generative Flows
Why Normalizing Flows Fail to Detect Out-of-Distribution Data
The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification
Regret in Online Recommendation Systems
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering
Myersonian Regression
On Convergence of Nearest Neighbor Classifiers over Feature Transformations
Learning Utilities and Equilibria in Non-Truthful Auctions
Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming
Secretary and Online Matching Problems with Machine Learned Advice
On the Error Resistance of Hinge-Loss Minimization
Polynomial-Time Computation of Optimal Correlated Equilibria in Two-Player Extensive-Form Games with Public Chance Moves and Beyond
Chaos, Extremism and Optimism: Volume Analysis of Learning in Games
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling
A Bandit Learning Algorithm and Applications to Auction Design
No-regret Learning in Price Competitions under Consumer Reference Effects
Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization
Partially View-aligned Clustering
MeshSDF: Differentiable Iso-Surface Extraction
Joint Policy Search for Multi-agent Collaboration with Imperfect Information
On Infinite-Width Hypernetworks
Training Generative Adversarial Networks with Limited Data
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing
A Self-Tuning Actor-Critic Algorithm
Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis
See, Hear, Explore: Curiosity via Audio-Visual Association
Finite-Time Analysis for Double Q-learning
Adaptive Discretization for Model-Based Reinforcement Learning
Object Goal Navigation using Goal-Oriented Semantic Exploration
Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity
A new convergent variant of Q-learning with linear function approximation
A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms
Adversarially Robust Streaming Algorithms via Differential Privacy
Generalized Boosting
A Topological Filter for Learning with Label Noise
Learning by Minimizing the Sum of Ranked Range
Partial Optimal Transport with applications on Positive-Unlabeled Learning
Assisted Learning: A Framework for Multi-Organization Learning
Learning Disentangled Representations and Group Structure of Dynamical Environments
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention
COBE: Contextualized Object Embeddings from Narrated Instructional Video
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?
Probably Approximately Correct Constrained Learning
Sharp uniform convergence bounds through empirical centralization
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs
Gaussian Gated Linear Networks
BayReL: Bayesian Relational Learning for Multi-omics Data Integration
Manifold structure in graph embeddings
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
Learning to Learn with Feedback and Local Plasticity
A Theoretical Framework for Target Propagation
Inductive Quantum Embedding
Optimizing Neural Networks via Koopman Operator Theory
Biological credit assignment through dynamic inversion of feedforward networks
On 1/n neural representation and robustness
Real World Games Look Like Spinning Tops
Quantitative Propagation of Chaos for SGD in Wide Neural Networks
Network size and size of the weights in memorization with two-layers neural networks
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation
Soft Contrastive Learning for Visual Localization
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding
Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency
Cross-Scale Internal Graph Neural Network for Image Super-Resolution
GPS-Net: Graph-based Photometric Stereo Network
Convolutional Generation of Textured 3D Meshes
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective
Neural Sparse Representation for Image Restoration
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks
Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision
Learning Semantic-aware Normalization for Generative Adversarial Networks
3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data
Learning to Detect Objects with a 1 Megapixel Event Camera
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model
GANSpace: Discovering Interpretable GAN Controls
Deep Energy-based Modeling of Discrete-Time Physics
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory
Weak Form Generalized Hamiltonian Learning
Disentangling by Subspace Diffusion
Simultaneous Preference and Metric Learning from Paired Comparisons
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks
Hold me tight! Influence of discriminative features on deep network boundaries
Training Generative Adversarial Networks by Solving Ordinary Differential Equations
Sparse Graphical Memory for Robust Planning
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
Bayesian Robust Optimization for Imitation Learning
Learning Parities with Neural Networks
Learning the Linear Quadratic Regulator from Nonlinear Observations
Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms
Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes
On the Theory of Transfer Learning: The Importance of Task Diversity
Online Agnostic Boosting via Regret Minimization
Minimax Classification with 0-1 Loss and Performance Guarantees
Robust Density Estimation under Besov IPM Losses
Online Multitask Learning with Long-Term Memory
Improving Local Identifiability in Probabilistic Box Embeddings
Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes
Synthetic Data Generators -- Sequential and Private
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals
Statistical-Query Lower Bounds via Functional Gradients
PAC-Bayes Learning Bounds for Sample-Dependent Priors
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms
Decision trees as partitioning machines to characterize their generalization properties
A Limitation of the PAC-Bayes Framework
Conditioning and Processing: Techniques to Improve Information-Theoretic Generalization Bounds
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote
PAC-Bayes Analysis Beyond the Usual Bounds
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness
Probabilistic Orientation Estimation with Matrix Fisher Distributions
Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation
Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring
Calibrating CNNs for Lifelong Learning
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
Diverse Image Captioning with Context-Object Split Latent Spaces
An Analysis of SVD for Deep Rotation Estimation
DISK: Learning local features with policy gradient
Wasserstein Distances for Stereo Disparity Estimation
GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law
A Dictionary Approach to Domain-Invariant Learning in Deep Networks
Balanced Meta-Softmax for Long-Tailed Visual Recognition
Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders
Sparse Symplectically Integrated Neural Networks
Node Embeddings and Exact Low-Rank Representations of Complex Networks
Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology
Towards Understanding Hierarchical Learning: Benefits of Neural Representations
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes
Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets
Universal guarantees for decision tree induction via a higher-order splitting criterion
Learning Restricted Boltzmann Machines with Sparse Latent Variables
The Adaptive Complexity of Maximizing a Gross Substitutes Valuation
Hedging in games: Faster convergence of external and swap regrets
Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative Priors
In search of robust measures of generalization
On Convergence and Generalization of Dropout Training
Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval
Correspondence learning via linearly-invariant embedding
PIE-NET: Parametric Inference of Point Cloud Edges
Neural Non-Rigid Tracking
Continuous Surface Embeddings
Learning to Orient Surfaces by Self-supervised Spherical CNNs
SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images
Neural Unsigned Distance Fields for Implicit Function Learning
Skeleton-bridged Point Completion: From Global Inference to Local Adjustment
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport
Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs
3D Shape Reconstruction from Vision and Touch
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
Neural Sparse Voxel Fields
RepPoints v2: Verification Meets Regression for Object Detection
Efficient Contextual Bandits with Continuous Actions
Collapsing Bandits and Their Application to Public Health Intervention
Learning to Play Sequential Games versus Unknown Opponents
Interferobot: aligning an optical interferometer by a reinforcement learning agent
Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting
Reinforcement Learning with Feedback Graphs
A Unifying View of Optimism in Episodic Reinforcement Learning
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration
On Reward-Free Reinforcement Learning with Linear Function Approximation
On Efficiency in Hierarchical Reinforcement Learning
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
Belief-Dependent Macro-Action Discovery in POMDPs using the Value of Information
High-Throughput Synchronous Deep RL
AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control
Dynamic Submodular Maximization
Adaptive Shrinkage Estimation for Streaming Graphs
Near-Optimal Comparison Based Clustering
Impossibility Results for Grammar-Compressed Linear Algebra
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso
Submodular Meta-Learning
Fairness in Streaming Submodular Maximization: Algorithms and Hardness
Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
Efficient active learning of sparse halfspaces with arbitrary bounded noise
Learning Structured Distributions From Untrusted Batches: Faster and Simpler
Outlier Robust Mean Estimation with Subgaussian Rates via Stability
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications
Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics
PAC-Bayesian Bound for the Conditional Value at Risk
Universal Function Approximation on Graphs
Model Class Reliance for Random Forests
Hypersolvers: Toward Fast Continuous-Depth Models
Almost Surely Stable Deep Dynamics
Learning Optimal Representations with the Decodable Information Bottleneck
Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning
Measuring Systematic Generalization in Neural Proof Generation with Transformers
Online Decision Based Visual Tracking via Reinforcement Learning
On the Modularity of Hypernetworks
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point
Counterexample-Guided Learning of Monotonic Neural Networks
Permute-and-Flip: A new mechanism for differentially private selection
No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
Spike and slab variational Bayes for high dimensional logistic regression
How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks?
Learning discrete distributions with infinite support
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs
Instance-based Generalization in Reinforcement Learning
Efficient Planning in Large MDPs with Weak Linear Function Approximation
Multi-agent active perception with prediction rewards
Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
Model-based Policy Optimization with Unsupervised Model Adaptation
Deep Reinforcement and InfoMax Learning
Zap Q-Learning With Nonlinear Function Approximation
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits
Self-Supervised MultiModal Versatile Networks
Deep Subspace Clustering with Data Augmentation
Learning Retrospective Knowledge with Reverse Reinforcement Learning
The MAGICAL Benchmark for Robust Imitation
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation
Counterfactual Data Augmentation using Locally Factored Dynamics
POMDPs in Continuous Time and Discrete Spaces
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
Forethought and Hindsight in Credit Assignment
Learning Multi-Agent Communication through Structured Attentive Reasoning
MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation
Influence-Augmented Online Planning for Complex Environments
Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms
Provably Efficient Neural GTD for Off-Policy Learning
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
Comparator-Adaptive Convex Bandits
(ends 11:00 AM)
noon
Symposium:
COVID-19 Symposium Day 1
(ends 4:00 PM)
Tutorial:
(Track3) Offline Reinforcement Learning: From Algorithm Design to Practical Applications Q&A
(ends 12:50 PM)
1 p.m.
Tutorial:
(Track1) Sketching and Streaming Algorithms Q&A
(ends 1:50 PM)
2 p.m.
Tutorial:
(Track2) Beyond Accuracy: Grounding Evaluation Metrics for Human-Machine Learning Systems Q&A
(ends 2:50 PM)
5 p.m.
Invited Talk:
Robustness, Verification, Privacy: Addressing Machine Learning Adversaries
Shafi Goldwasser
(ends 7:00 PM)
6 p.m.
Orals & Spotlights Track 12: Vision Applications [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Space-Time Correspondence as a Contrastive Random Walk
[6:15] Rethinking Pre-training and Self-training
[6:30] Do Adversarially Robust ImageNet Models Transfer Better?
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Self-Supervised Visual Representation Learning from Hierarchical Grouping
[7:10] Learning Affordance Landscapes for Interaction Exploration in 3D Environments
[7:20] Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D
[7:30] Large-Scale Adversarial Training for Vision-and-Language Representation Learning
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:20
[7:50] Measuring Robustness to Natural Distribution Shifts in Image Classification
[8:00] Curriculum By Smoothing
[8:10] Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies
Q&A s 8:20-8:30
[8:20] Joint Q&A for Preceeding Spotlights
Break s 8:30-9:00
Break
(ends 9:00 PM)
Demonstrations 2 [6:00-9:20]
(duration 3.3 hr)
Orals & Spotlights Track 13: Deep Learning/Theory [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Implicit Neural Representations with Periodic Activation Functions
[6:15] Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
[6:30] Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
[7:10] Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization
[7:20] Compositional Visual Generation with Energy Based Models
[7:30] Certified Monotonic Neural Networks
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing
[8:00] On Correctness of Automatic Differentiation for Non-Differentiable Functions
[8:10] The Complete Lasso Tradeoff Diagram
[8:20] Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 PM)
Orals & Spotlights Track 14: Reinforcement Learning [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement
[6:15] Learning Individually Inferred Communication for Multi-Agent Cooperation
[6:30] Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Reinforcement Learning with Augmented Data
[7:10] Sub-sampling for Efficient Non-Parametric Bandit Exploration
[7:20] Language-Conditioned Imitation Learning for Robot Manipulation Tasks
[7:30] High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Policy Improvement via Imitation of Multiple Oracles
[8:00] Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning
[8:10] Avoiding Side Effects in Complex Environments
[8:20] Preference-based Reinforcement Learning with Finite-Time Guarantees
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 PM)
9 p.m.
Poster Session 3 [9:00-11:00]
Conservative Q-Learning for Offline Reinforcement Learning
Towards Playing Full MOBA Games with Deep Reinforcement Learning
Federated Bayesian Optimization via Thompson Sampling
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
Reinforcement Learning with Augmented Data
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning
Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Succinct and Robust Multi-Agent Communication With Temporal Message Control
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward
Learning Individually Inferred Communication for Multi-Agent Cooperation
Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences
Marginal Utility for Planning in Continuous or Large Discrete Action Spaces
A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances
Softmax Deep Double Deterministic Policy Gradients
Non-Crossing Quantile Regression for Distributional Reinforcement Learning
Improving Generalization in Reinforcement Learning with Mixture Regularization
Choice Bandits
Differentiable Meta-Learning of Bandit Policies
Latent Bandits Revisited
Finite-Time Analysis of Round-Robin Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards
Sub-sampling for Efficient Non-Parametric Bandit Exploration
Online Learning in Contextual Bandits using Gated Linear Networks
High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization
Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement
RD$^2$: Reward Decomposition with Representation Decomposition
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization
Learning Guidance Rewards with Trajectory-space Smoothing
Avoiding Side Effects in Complex Environments
Reward-rational (implicit) choice: A unifying formalism for reward learning
Planning with General Objective Functions: Going Beyond Total Rewards
Preference-based Reinforcement Learning with Finite-Time Guarantees
Is Long Horizon RL More Difficult Than Short Horizon RL?
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
Error Bounds of Imitating Policies and Environments
Model-based Adversarial Meta-Reinforcement Learning
Offline Imitation Learning with a Misspecified Simulator
Policy Improvement via Imitation of Multiple Oracles
Toward the Fundamental Limits of Imitation Learning
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
Multi-task Batch Reinforcement Learning with Metric Learning
Multi-Task Reinforcement Learning with Soft Modularization
Generalized Hindsight for Reinforcement Learning
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning
Steady State Analysis of Episodic Reinforcement Learning
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
Learning Disentangled Representations of Videos with Missing Data
Cycle-Contrast for Self-Supervised Video Representation Learning
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies
Blind Video Temporal Consistency via Deep Video Prior
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample
Space-Time Correspondence as a Contrastive Random Walk
Do Adversarially Robust ImageNet Models Transfer Better?
Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement
Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes
COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
Stochastic Normalization
Curriculum By Smoothing
Focus of Attention Improves Information Transfer in Visual Features
Semantic Visual Navigation by Watching YouTube Videos
Lipschitz-Certifiable Training with a Tight Outer Bound
Efficient Exact Verification of Binarized Neural Networks
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
CASTLE: Regularization via Auxiliary Causal Graph Discovery
Multi-Stage Influence Function
On Completeness-aware Concept-Based Explanations in Deep Neural Networks
Adaptive Online Estimation of Piecewise Polynomial Trends
Robust Optimization for Fairness with Noisy Protected Groups
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses
The Discrete Gaussian for Differential Privacy
Locally Differentially Private (Contextual) Bandits Learning
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
Privacy Amplification via Random Check-Ins
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space
Breaking the Communication-Privacy-Accuracy Trilemma
Towards practical differentially private causal graph discovery
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization
Relative gradient optimization of the Jacobian term in unsupervised deep learning
Multipole Graph Neural Operator for Parametric Partial Differential Equations
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
Proximity Operator of the Matrix Perspective Function and its Applications
Improved Algorithms for Convex-Concave Minimax Optimization
Decentralized Accelerated Proximal Gradient Descent
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization
Lower Bounds and Optimal Algorithms for Personalized Federated Learning
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees
A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization
Subgroup-based Rank-1 Lattice Quasi-Monte Carlo
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
Optimal Query Complexity of Secure Stochastic Convex Optimization
Approximate Cross-Validation with Low-Rank Data in High Dimensions
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes
A Closer Look at Accuracy vs. Robustness
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free
Adversarial Distributional Training for Robust Deep Learning
On the Trade-off between Adversarial and Backdoor Robustness
An Efficient Adversarial Attack for Tree Ensembles
Adversarial Self-Supervised Contrastive Learning
Adversarial Weight Perturbation Helps Robust Generalization
Large-Scale Adversarial Training for Vision-and-Language Representation Learning
GreedyFool: Distortion-Aware Sparse Adversarial Attack
Consistency Regularization for Certified Robustness of Smoothed Classifiers
Measuring Robustness to Natural Distribution Shifts in Image Classification
Certified Monotonic Neural Networks
Backpropagating Linearly Improves Transferability of Adversarial Examples
Practical No-box Adversarial Attacks against DNNs
Learning to Adapt to Evolving Domains
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
Heuristic Domain Adaptation
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
Implicit Neural Representations with Periodic Activation Functions
Rethinking Pre-training and Self-training
MetaSDF: Meta-Learning Signed Distance Functions
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
Deep Automodulators
Autoregressive Score Matching
Compositional Visual Generation with Energy Based Models
How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions
Domain Adaptation as a Problem of Inference on Graphical Models
Fast Unbalanced Optimal Transport on a Tree
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers
A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions
Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework
Preference learning along multiple criteria: A game-theoretic perspective
On Correctness of Automatic Differentiation for Non-Differentiable Functions
Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time
Cooperative Multi-player Bandit Optimization
Neutralizing Self-Selection Bias in Sampling for Sortition
The Complete Lasso Tradeoff Diagram
Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality
Distributional Robustness with IPMs and links to Regularization and GANs
Towards Convergence Rate Analysis of Random Forests for Classification
Learning to Mutate with Hypergradient Guided Population
Robust Disentanglement of a Few Factors at a Time using rPU-VAE
Self-Supervised Graph Transformer on Large-Scale Molecular Data
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation
Towards Interpretable Natural Language Understanding with Explanations as Latent Variables
Learning to summarize with human feedback
Language-Conditioned Imitation Learning for Robot Manipulation Tasks
Guiding Deep Molecular Optimization with Genetic Exploration
What is being transferred in transfer learning?
What shapes feature representations? Exploring datasets, architectures, and training
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
Benchmarking Deep Learning Interpretability in Time Series Predictions
Stochastic Deep Gaussian Processes over Graphs
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
Rethinking Learnable Tree Filter for Generic Feature Transform
SOLOv2: Dynamic and Fast Instance Segmentation
HOI Analysis: Integrating and Decomposing Human-Object Interaction
RANet: Region Attention Network for Semantic Segmentation
ICNet: Intra-saliency Correlation Network for Co-Saliency Detection
Few-Cost Salient Object Detection with Adversarial-Paced Learning
Detecting Hands and Recognizing Physical Contact in the Wild
Targeted Adversarial Perturbations for Monocular Depth Prediction
Self-Supervised Visual Representation Learning from Hierarchical Grouping
Learning Affordance Landscapes for Interaction Exploration in 3D Environments
Deep Variational Instance Segmentation
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation
Fine-Grained Dynamic Head for Object Detection
Learning About Objects by Learning to Interact with Them
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D
Optimal visual search based on a model of target detectability in natural images
Online Influence Maximization under Linear Threshold Model
Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions
(ends 11:00 PM)
WED 9 DEC
1 a.m.
Tutorial:
(Track1) Advances in Approximate Inference Q&A
(ends 1:50 AM)
1:40 a.m.
Affinity Workshop:
Women in Machine Learning
(ends 6:00 PM)
3 a.m.
Tutorial:
(Track2) Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities Q&A
(ends 3:50 AM)
5 a.m.
Invited Talk (Posner Lecture):
The Real AI Revolution
Chris Bishop
(ends 7:00 AM)
6 a.m.
Demonstrations 1 [6:00-9:20]
(duration 3.3 hr)
Orals & Spotlights Track 15: COVID/Applications/Composition [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] High-Fidelity Generative Image Compression
[6:15] Learning Composable Energy Surrogates for PDE Order Reduction
[6:30] Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Compositional Generalization by Learning Analytical Expressions
[7:10] Modern Hopfield Networks and Attention for Immune Repertoire Classification
[7:20] ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA
[7:30] A causal view of compositional zero-shot recognition
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:40
[7:50] RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist
[8:00] Barking up the right tree: an approach to search over molecule synthesis DAGs
[8:10] Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views
[8:20] Experimental design for MRI by greedy policy search
[8:30] How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?
Q&A s 8:40-8:50
[8:40] Joint Q&A for Preceeding Spotlights
Break s 8:50-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 16: Continual/Meta/Misc Learning [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Continual Deep Learning by Functional Regularisation of Memorable Past
[6:15] Look-ahead Meta Learning for Continual Learning
[6:30] NeuMiss networks: differentiable programming for supervised learning with missing values.
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Meta-trained agents implement Bayes-optimal agents
[7:10] Linear Dynamical Systems as a Core Computational Primitive
[7:20] Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
[7:30] Uncertainty-aware Self-training for Few-shot Text Classification
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:40
[7:50] HiPPO: Recurrent Memory with Optimal Polynomial Projections
[8:00] Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity
[8:10] Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge
[8:20] Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning
[8:30] Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients
Q&A s 8:40-8:50
[8:40] Joint Q&A for Preceeding Spotlights
Break s 8:50-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 17: Kernel Methods/Optimization [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces
[6:15] Kernel Methods Through the Roof: Handling Billions of Points Efficiently
[6:30] A Group-Theoretic Framework for Data Augmentation
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] A mathematical model for automatic differentiation in machine learning
[7:10] A kernel test for quasi-independence
[7:20] Fourier Sparse Leverage Scores and Approximate Kernel Learning
[7:30] BOSS: Bayesian Optimization over String Spaces
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Fast geometric learning with symbolic matrices
[8:00] Training Stronger Baselines for Learning to Optimize
[8:10] Learning Linear Programs from Optimal Decisions
[8:20] Automatically Learning Compact Quality-aware Surrogates for Optimization Problems
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 18: Deep Learning [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Ultra-Low Precision 4-bit Training of Deep Neural Networks
[6:15] Reservoir Computing meets Recurrent Kernels and Structured Transforms
[6:30] The interplay between randomness and structure during learning in RNNs
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] What if Neural Networks had SVDs?
[7:10] Practical Quasi-Newton Methods for Training Deep Neural Networks
[7:20] Triple descent and the two kinds of overfitting: where & why do they appear?
[7:30] On the linearity of large non-linear models: when and why the tangent kernel is constant
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:20
[7:50] Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy
[8:00] Proximal Mapping for Deep Regularization
[8:10] BoxE: A Box Embedding Model for Knowledge Base Completion
Q&A s 8:20-8:30
[8:20] Joint Q&A for Preceeding Spotlights
Break s 8:30-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 19: Probabilistic/Causality [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Network-to-Network Translation with Conditional Invertible Neural Networks
[6:15] Causal Imitation Learning With Unobserved Confounders
[6:30] Gradient Estimation with Stochastic Softmax Tricks
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs
[7:10] A Randomized Algorithm to Reduce the Support of Discrete Measures
[7:20] A/B Testing in Dense Large-Scale Networks: Design and Inference
[7:30] DisARM: An Antithetic Gradient Estimator for Binary Latent Variables
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:40
[7:50] Off-Policy Evaluation and Learning for External Validity under a Covariate Shift
[8:00] Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
[8:10] Differentiable Causal Discovery from Interventional Data
[8:20] Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks
[8:30] Efficient semidefinite-programming-based inference for binary and multi-class MRFs
Q&A s 8:40-8:50
[8:40] Joint Q&A for Preceeding Spotlights
Break s 8:50-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 20: Social/Adversarial Learning [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
[6:15] Metric-Free Individual Fairness in Online Learning
[6:30] Fair regression via plug-in estimator and recalibration with statistical guarantees
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay
[7:10] Differentially-Private Federated Linear Bandits
[7:20] Adversarial Training is a Form of Data-dependent Operator Norm Regularization
[7:30] Prediction with Corrupted Expert Advice
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:40
[7:50] Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
[8:00] Towards Safe Policy Improvement for Non-Stationary MDPs
[8:10] Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations
[8:20] Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
[8:30] Understanding Gradient Clipping in Private SGD: A Geometric Perspective
Q&A s 8:40-8:50
[8:40] Joint Q&A for Preceeding Spotlights
Break s 8:50-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 21: Optimization [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-7:00
[6:00] Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
[6:15] Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form
[6:30] Acceleration with a Ball Optimization Oracle
[6:45] Convex optimization based on global lower second-order models
Spotlight s 7:00-7:40
[7:00] Adam with Bandit Sampling for Deep Learning
[7:10] Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling
[7:20] IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
[7:30] Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:40
[7:50] Minibatch Stochastic Approximate Proximal Point Methods
[8:00] Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems
[8:10] Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
[8:20] Linearly Converging Error Compensated SGD
[8:30] Learning Augmented Energy Minimization via Speed Scaling
Q&A s 8:40-8:50
[8:40] Joint Q&A for Preceeding Spotlights
Break s 8:50-9:00
Break
(ends 9:00 AM)
9 a.m.
Poster Session 4 [9:00-11:00]
Model Fusion via Optimal Transport
What if Neural Networks had SVDs?
Understanding and Improving Fast Adversarial Training
Posterior Re-calibration for Imbalanced Datasets
Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout
GCN meets GPU: Decoupling “When to Sample” from “How to Sample”
Improving model calibration with accuracy versus uncertainty optimization
Deep Evidential Regression
Practical Quasi-Newton Methods for Training Deep Neural Networks
Ultra-Low Precision 4-bit Training of Deep Neural Networks
Improving Neural Network Training in Low Dimensional Random Bases
Bandit Samplers for Training Graph Neural Networks
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods
MomentumRNN: Integrating Momentum into Recurrent Neural Networks
Why are Adaptive Methods Good for Attention Models?
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
Dark Experience for General Continual Learning: a Strong, Simple Baseline
RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning
Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks
Continual Deep Learning by Functional Regularisation of Memorable Past
Look-ahead Meta Learning for Continual Learning
A Combinatorial Perspective on Transfer Learning
Continual Learning in Low-rank Orthogonal Subspaces
Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
Task-Robust Model-Agnostic Meta-Learning
Learning to Learn Variational Semantic Memory
Continuous Meta-Learning without Tasks
Auxiliary Task Reweighting for Minimum-data Learning
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems
Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data
PLANS: Neuro-Symbolic Program Learning from Videos
Probabilistic Linear Solvers for Machine Learning
A/B Testing in Dense Large-Scale Networks: Design and Inference
Dual Instrumental Variable Regression
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks
Gradient Regularized V-Learning for Dynamic Treatment Regimes
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models
Causal Estimation with Functional Confounders
Counterfactual Prediction for Bundle Treatment
Minimax Estimation of Conditional Moment Models
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
Multi-task Causal Learning with Gaussian Processes
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Compositional Generalization via Neural-Symbolic Stack Machines
Learning Invariants through Soft Unification
Linear Disentangled Representations and Unsupervised Action Estimation
Identifying Mislabeled Data using the Area Under the Margin Ranking
A Bayesian Nonparametrics View into Deep Representations
Learning Invariances in Neural Networks from Training Data
Inverse Learning of Symmetries
Post-training Iterative Hierarchical Data Augmentation for Deep Networks
Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation
Goal-directed Generation of Discrete Structures with Conditional Generative Models
CoSE: Compositional Stroke Embeddings
A Causal View on Robustness of Neural Networks
Regularizing Towards Permutation Invariance In Recurrent Models
STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks
Generative causal explanations of black-box classifiers
Assessing SATNet's Ability to Solve the Symbol Grounding Problem
Towards Better Generalization of Adaptive Gradient Methods
Adam with Bandit Sampling for Deep Learning
Random Reshuffling: Simple Analysis with Vast Improvements
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates
A Catalyst Framework for Minimax Optimization
Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function
An Improved Analysis of Stochastic Gradient Descent with Momentum
Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations
Online Robust Regression via SGD on the l1 loss
Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping
Large-Scale Methods for Distributionally Robust Optimization
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems
Provable Overlapping Community Detection in Weighted Graphs
Improving Inference for Neural Image Compression
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning
Kernel Methods Through the Roof: Handling Billions of Points Efficiently
Discovering conflicting groups in signed networks
StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks
Learning Differentiable Programs with Admissible Neural Heuristics
Neural Execution Engines: Learning to Execute Subroutines
Multimodal Graph Networks for Compositional Generalization in Visual Question Answering
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology
Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients
Fast geometric learning with symbolic matrices
Synbols: Probing Learning Algorithms with Synthetic Datasets
Evaluating Attribution for Graph Neural Networks
Accelerating Reinforcement Learning through GPU Atari Emulation
Adaptive Gradient Quantization for Data-Parallel SGD
Universally Quantized Neural Compression
Searching for Low-Bit Weights in Quantized Neural Networks
Bayesian Bits: Unifying Quantization and Pruning
FleXOR: Trainable Fractional Quantization
Robust Quantization: One Model to Rule Them All
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables
Efficient semidefinite-programming-based inference for binary and multi-class MRFs
Gradient Estimation with Stochastic Softmax Tricks
Quantized Variational Inference
Approximation Based Variance Reduction for Reparameterization Gradients
VarGrad: A Low-Variance Gradient Estimator for Variational Inference
Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds
AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference
Group-Fair Online Allocation in Continuous Time
Fair Hierarchical Clustering
Approximate Heavily-Constrained Learning with Lagrange Multiplier Models
Metric-Free Individual Fairness in Online Learning
Fair regression via plug-in estimator and recalibration with statistical guarantees
Fair Multiple Decision Making Through Soft Interventions
Intra-Processing Methods for Debiasing Neural Networks
Ensuring Fairness Beyond the Training Data
Fair Performance Metric Elicitation
Fairness without Demographics through Adversarially Reweighted Learning
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning
How do fair decisions fare in long-term qualification?
Fair regression with Wasserstein barycenters
Learning Certified Individually Fair Representations
Fairness with Overlapping Groups; a Probabilistic Perspective
Consistent Plug-in Classifiers for Complex Objectives and Constraints
Causal Discovery in Physical Systems from Videos
Causal Imitation Learning With Unobserved Confounders
A Class of Algorithms for General Instrumental Variable Models
Active Invariant Causal Prediction: Experiment Selection through Stability
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
Deep Structural Causal Models for Tractable Counterfactual Inference
Reconsidering Generative Objectives For Counterfactual Reasoning
High-recall causal discovery for autocorrelated time series with latent confounders
Applications of Common Entropy for Causal Inference
Entropic Causal Inference: Identifiability and Finite Sample Results
General Transportability of Soft Interventions: Completeness Results
Learning Causal Effects via Weighted Empirical Risk Minimization
Differentiable Causal Discovery from Interventional Data
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks
A polynomial-time algorithm for learning nonparametric causal graphs
Linear Time Sinkhorn Divergences using Positive Features
Learning Kernel Tests Without Data Splitting
Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces
A kernel test for quasi-independence
Hard Shape-Constrained Kernel Machines
Statistical Optimal Transport posed as Learning Kernel Embedding
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search
Continuous Regularized Wasserstein Barycenters
Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form
Multi-Fidelity Bayesian Optimization via Deep Neural Networks
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization
Hybrid Models for Learning to Branch
Interior Point Solving for LP-based prediction+optimisation
Curriculum learning for multilevel budgeted combinatorial problems
BOSS: Bayesian Optimization over String Spaces
An implicit function learning approach for parametric modal regression
Adversarial Example Games
Robust Pre-Training by Adversarial Contrastive Learning
Provably Robust Metric Learning
A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses
Adversarial Training is a Form of Data-dependent Operator Norm Regularization
Adversarial Robustness of Supervised Sparse Coding
Boosting Adversarial Training with Hypersphere Embedding
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability
Robustness of Bayesian Neural Networks to Gradient-Based Attacks
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations
On Adaptive Attacks to Adversarial Example Defenses
High-Fidelity Generative Image Compression
Attribute Prototype Network for Zero-Shot Learning
Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies
Variational Interaction Information Maximization for Cross-domain Disentanglement
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning
Few-shot Image Generation with Elastic Weight Consolidation
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs
Generating Correct Answers for Progressive Matrices Intelligence Tests
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars
Network-to-Network Translation with Conditional Invertible Neural Networks
Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation
Instance Selection for GANs
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping
Smoothed Geometry for Robust Attribution
Neural Networks with Recurrent Generative Feedback
COT-GAN: Generating Sequential Data via Causal Optimal Transport
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA
Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control
Implicit Rank-Minimizing Autoencoder
Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data
Set2Graph: Learning Graphs From Sets
Efficient Generation of Structured Objects with Constrained Adversarial Networks
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting
Regularized linear autoencoders recover the principal components, eventually
BoxE: A Box Embedding Model for Knowledge Base Completion
BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images
Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views
Deep Statistical Solvers
Learning of Discrete Graphical Models with Neural Networks
Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity
Falcon: Fast Spectral Inference on Encrypted Data
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers
Learning Composable Energy Surrogates for PDE Order Reduction
AvE: Assistance via Empowerment
EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints
RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist
Barking up the right tree: an approach to search over molecule synthesis DAGs
Synthesizing Tasks for Block-based Programming
Deep Imitation Learning for Bimanual Robotic Manipulation
PRANK: motion Prediction based on RANKing
Meta-trained agents implement Bayes-optimal agents
On the equivalence of molecular graph convolution and molecular wave function with poor basis set
Dynamic allocation of limited memory resources in reinforcement learning
Ensembling geophysical models with Bayesian Neural Networks
Neurosymbolic Transformers for Multi-Agent Communication
Avoiding Side Effects By Considering Future Tasks
What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals
Learning Mutational Semantics
Zero-Resource Knowledge-Grounded Dialogue Generation
Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatment
Causal analysis of Covid-19 Spread in Germany
Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains
X-CAL: Explicit Calibration for Survival Analysis
Experimental design for MRI by greedy policy search
How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?
OrganITE: Optimal transplant donor organ offering using an individual treatment effect
When Counterpoint Meets Chinese Folk Melodies
BERT Loses Patience: Fast and Robust Inference with Early Exit
Unsupervised Text Generation by Learning from Search
Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge
A Spectral Energy Distance for Parallel Speech Synthesis
Compositional Generalization by Learning Analytical Expressions
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings
Kernel Alignment Risk Estimator: Risk Prediction from Training Data
Fourier Sparse Leverage Scores and Approximate Kernel Learning
Demystifying Orthogonal Monte Carlo and Beyond
Unfolding recurrence by Green’s functions for optimized reservoir computing
A Group-Theoretic Framework for Data Augmentation
Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition
Triple descent and the two kinds of overfitting: where & why do they appear?
The interplay between randomness and structure during learning in RNNs
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent
When Do Neural Networks Outperform Kernel Methods?
The Statistical Cost of Robust Kernel Hyperparameter Turning
Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model
Randomized tests for high-dimensional regression: A more efficient and powerful solution
Sample complexity and effective dimension for regression on manifolds
An analytic theory of shallow networks dynamics for hinge loss classification
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
One-bit Supervision for Image Classification
Your Classifier can Secretly Suffice Multi-Source Domain Adaptation
Early-Learning Regularization Prevents Memorization of Noisy Labels
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition
Universal Domain Adaptation through Self Supervision
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
A causal view of compositional zero-shot recognition
CompRess: Self-Supervised Learning by Compressing Representations
Big Self-Supervised Models are Strong Semi-Supervised Learners
Provably Consistent Partial-Label Learning
Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning
Unsupervised Translation of Programming Languages
Uncertainty-aware Self-training for Few-shot Text Classification
Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching
Teaching a GAN What Not to Learn
A Randomized Algorithm to Reduce the Support of Discrete Measures
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games
Better Full-Matrix Regret via Parameter-Free Online Learning
Locally-Adaptive Nonparametric Online Learning
Online Learning with Primary and Secondary Losses
Online Linear Optimization with Many Hints
Exploiting the Surrogate Gap in Online Multiclass Classification
Temporal Variability in Implicit Online Learning
Prediction with Corrupted Expert Advice
A mathematical model for automatic differentiation in machine learning
Online Non-Convex Optimization with Imperfect Feedback
Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes
Differentially-Private Federated Linear Bandits
Learning Strategy-Aware Linear Classifiers
BRP-NAS: Prediction-based NAS using GCNs
Neural Architecture Generator Optimization
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning
Agnostic Learning with Multiple Objectives
Training Stronger Baselines for Learning to Optimize
GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
Geometric Dataset Distances via Optimal Transport
Efficient Algorithms for Device Placement of DNN Graph Operators
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems
Bayesian Optimization for Iterative Learning
Model Selection for Production System via Automated Online Experiments
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations
CryptoNAS: Private Inference on a ReLU Budget
Neural Power Units
Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations
High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds
Learning Feature Sparse Principal Subspace
Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine
Stochastic Optimization with Laggard Data Pipelines
Black-Box Optimization with Local Generative Surrogates
Learning Linear Programs from Optimal Decisions
Acceleration with a Ball Optimization Oracle
Convex optimization based on global lower second-order models
Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
An efficient nonconvex reformulation of stagewise convex optimization problems
Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
First-Order Methods for Large-Scale Market Equilibrium Computation
Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay
Deep Smoothing of the Implied Volatility Surface
What went wrong and when? Instance-wise feature importance for time-series black-box models
Learning from Failure: De-biasing Classifier from Biased Classifier
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability
Learning Global Transparent Models consistent with Local Contrastive Explanations
Towards Safe Policy Improvement for Non-Stationary MDPs
Decisions, Counterfactual Explanations and Strategic Behavior
From Predictions to Decisions: Using Lookahead Regularization
Model Agnostic Multilevel Explanations
Achieving Equalized Odds by Resampling Sensitive Attributes
Regularizing Black-box Models for Improved Interpretability
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
MetaPoison: Practical General-purpose Clean-label Data Poisoning
Understanding Gradient Clipping in Private SGD: A Geometric Perspective
Coresets via Bilevel Optimization for Continual Learning and Streaming
Linear Dynamical Systems as a Core Computational Primitive
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits
Sliding Window Algorithms for k-Clustering Problems
Fast and Accurate $k$-means++ via Rejection Sampling
NeuMiss networks: differentiable programming for supervised learning with missing values.
Debiasing Averaged Stochastic Gradient Descent to handle missing values
Coresets for Near-Convex Functions
HiPPO: Recurrent Memory with Optimal Polynomial Projections
Online MAP Inference of Determinantal Point Processes
Joints in Random Forests
Approximate Cross-Validation for Structured Models
A convex optimization formulation for multivariate regression
Adaptive Reduced Rank Regression
Self-Distillation Amplifies Regularization in Hilbert Space
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective
Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning
Explicit Regularisation in Gaussian Noise Injections
Rational neural networks
On the Similarity between the Laplace and Neural Tangent Kernels
Neural Anisotropy Directions
Ode to an ODE
Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity
Limits to Depth Efficiencies of Self-Attention
On the linearity of large non-linear models: when and why the tangent kernel is constant
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy
Directional Pruning of Deep Neural Networks
Winning the Lottery with Continuous Sparsification
Analytic Characterization of the Hessian in Shallow ReLU Models: A Tale of Symmetry
On the universality of deep learning
Reservoir Computing meets Recurrent Kernels and Structured Transforms
The Diversified Ensemble Neural Network
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
Spin-Weighted Spherical CNNs
Autoencoders that don't overfit towards the Identity
Modern Hopfield Networks and Attention for Immune Repertoire Classification
UCSG-NET- Unsupervised Discovering of Constructive Solid Geometry Tree
UCLID-Net: Single View Reconstruction in Object Space
Graph Geometry Interaction Learning
Feature Importance Ranking for Deep Learning
Introducing Routing Uncertainty in Capsule Networks
The Pitfalls of Simplicity Bias in Neural Networks
Primal-Dual Mesh Convolutional Neural Networks
The Convolution Exponential and Generalized Sylvester Flows
Coherent Hierarchical Multi-Label Classification Networks
Differentiable Top-k with Optimal Transport
Proximal Mapping for Deep Regularization
CSER: Communication-efficient SGD with Error Reset
Practical Low-Rank Communication Compression in Decentralized Deep Learning
A Decentralized Parallel Algorithm for Training Generative Adversarial Nets
Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking
Distributed Newton Can Communicate Less and Resist Byzantine Workers
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
Dual-Free Stochastic Decentralized Optimization with Variance Reduction
FedSplit: an algorithmic framework for fast federated optimization
Distributionally Robust Federated Averaging
Personalized Federated Learning with Moreau Envelopes
Minibatch vs Local SGD for Heterogeneous Distributed Learning
Minibatch Stochastic Approximate Proximal Point Methods
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach
A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms
Linearly Converging Error Compensated SGD
The All-or-Nothing Phenomenon in Sparse Tensor PCA
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow
ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool
Normalizing Kalman Filters for Multivariate Time Series Analysis
Learning Augmented Energy Minimization via Speed Scaling
(ends 11:00 AM)
11 a.m.
Memorial:
In Memory of Olivier Chapelle
(ends 12:00 PM)
noon
Symposium:
COVID-19 Symposium Day 2
(ends 4:00 PM)
Tutorial:
(Track2) Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning Q&A
(ends 12:50 PM)
2 p.m.
Tutorial:
(Track1) Abstraction & Reasoning in AI systems: Modern Perspectives Q&A
(ends 2:50 PM)
5 p.m.
Invited Talk:
A Future of Work for the Invisible Workers in A.I.
Saiph Savage
(ends 7:00 PM)
6 p.m.
Demonstrations 2 [6:00-9:20]
(duration 3.3 hr)
Orals & Spotlights Track 22: Vision Applications [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence
[6:15] LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration
[6:30] The Origins and Prevalence of Texture Bias in Convolutional Neural Networks
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Distribution Matching for Crowd Counting
[7:10] Texture Interpolation for Probing Visual Perception
[7:20] Consistent Structural Relation Learning for Zero-Shot Segmentation
[7:30] CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] ShapeFlow: Learnable Deformation Flows Among 3D Shapes
[8:00] Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows
[8:10] Counterfactual Vision-and-Language Navigation: Unravelling the Unseen
[8:20] RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 PM)
Orals & Spotlights Track 23: Graph/Meta Learning/Software [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] FrugalML: How to use ML Prediction APIs more accurately and cheaply
[6:15] AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
[6:30] PyGlove: Symbolic Programming for Automated Machine Learning
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Improved Schemes for Episodic Memory-based Lifelong Learning
[7:10] Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
[7:20] Uncertainty Aware Semi-Supervised Learning on Graph Data
[7:30] Rethinking Importance Weighting for Deep Learning under Distribution Shift
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:20
[7:50] Modular Meta-Learning with Shrinkage
[8:00] JAX MD: A Framework for Differentiable Physics
[8:10] RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
Q&A s 8:20-8:30
[8:20] Joint Q&A for Preceeding Spotlights
Break s 8:30-9:00
Break
(ends 9:00 PM)
Orals & Spotlights Track 24: Learning Theory [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method
[6:15] Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs
[6:30] Worst-Case Analysis for Randomly Collected Data
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] On Adaptive Distance Estimation
[7:10] Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits
[7:20] Delay and Cooperation in Nonstochastic Linear Bandits
[7:30] Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:40
[7:50] Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
[8:00] A Tight Lower Bound and Efficient Reduction for Swap Regret
[8:10] Estimation of Skill Distribution from a Tournament
[8:20] Optimal Prediction of the Number of Unseen Species with Multiplicity
[8:30] Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks
Q&A s 8:40-8:50
[8:40] Joint Q&A for Preceeding Spotlights
Break s 8:50-9:00
Break
(ends 9:00 PM)
9 p.m.
Poster Session 5 [9:00-11:00]
Consistent Structural Relation Learning for Zero-Shot Segmentation
TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation
AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection
UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging
Group Contextual Encoding for 3D Point Clouds
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
Dual-Resolution Correspondence Networks
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation
Modeling Noisy Annotations for Crowd Counting
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
Distribution Matching for Crowd Counting
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation
Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization
RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder
Structured Convolutions for Efficient Neural Network Design
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models
Ultrahyperbolic Representation Learning
Reparameterizing Mirror Descent as Gradient Descent
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
Handling Missing Data with Graph Representation Learning
Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network
How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods
PEP: Parameter Ensembling by Perturbation
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models
Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks
Dirichlet Graph Variational Autoencoder
Gradient Boosted Normalizing Flows
Improved Guarantees for k-means++ and k-means++ Parallel
Estimation of Skill Distribution from a Tournament
Transfer Learning via $\ell_1$ Regularization
Robust Meta-learning for Mixed Linear Regression with Small Batches
Efficient Projection-free Algorithms for Saddle Point Problems
A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems
A Fair Classifier Using Kernel Density Estimation
List-Decodable Mean Estimation via Iterative Multi-Filtering
On Learning Ising Models under Huber's Contamination Model
Worst-Case Analysis for Randomly Collected Data
Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate
A General Method for Robust Learning from Batches
Optimal Prediction of the Number of Unseen Species with Multiplicity
Probabilistic Active Meta-Learning
Meta-Consolidation for Continual Learning
Understanding the Role of Training Regimes in Continual Learning
Supermasks in Superposition
Improved Schemes for Episodic Memory-based Lifelong Learning
Continual Learning with Node-Importance based Adaptive Group Sparse Regularization
GAN Memory with No Forgetting
Meta-Learning with Adaptive Hyperparameters
Online Structured Meta-learning
Modeling and Optimization Trade-off in Meta-learning
Structured Prediction for Conditional Meta-Learning
Meta-learning from Tasks with Heterogeneous Attribute Spaces
Gradient-EM Bayesian Meta-Learning
MATE: Plugging in Model Awareness to Task Embedding for Meta Learning
Neural Complexity Measures
Modular Meta-Learning with Shrinkage
Gradient Surgery for Multi-Task Learning
Organizing recurrent network dynamics by task-computation to enable continual learning
Finding the Homology of Decision Boundaries with Active Learning
Exemplar Guided Active Learning
OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification
An Unbiased Risk Estimator for Learning with Augmented Classes
Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization
Rethinking Importance Weighting for Deep Learning under Distribution Shift
Robust Correction of Sampling Bias using Cumulative Distribution Functions
Learning from Positive and Unlabeled Data with Arbitrary Positive Shift
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
A Variational Approach for Learning from Positive and Unlabeled Data
HRN: A Holistic Approach to One Class Learning
Rethinking the Value of Labels for Improving Class-Imbalanced Learning
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
Learning from Aggregate Observations
Self-training Avoids Using Spurious Features Under Domain Shift
Learning Physical Constraints with Neural Projections
Time-Reversal Symmetric ODE Network
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks
LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration
Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment
Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics
Counterfactual Vision-and-Language Navigation: Unravelling the Unseen
Neural FFTs for Universal Texture Image Synthesis
CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching
Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
JAX MD: A Framework for Differentiable Physics
Multi-agent Trajectory Prediction with Fuzzy Query Attention
Learning Agent Representations for Ice Hockey
Correlation Robust Influence Maximization
On Adaptive Distance Estimation
Recovery of sparse linear classifiers from mixture of responses
Robustness of Community Detection to Random Geometric Perturbations
Entrywise convergence of iterative methods for eigenproblems
Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform
Beyond Lazy Training for Over-parameterized Tensor Decomposition
Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method
Precise expressions for random projections: Low-rank approximation and randomized Newton
Matrix Completion with Hierarchical Graph Side Information
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices
Fairness constraints can help exact inference in structured prediction
Exact expressions for double descent and implicit regularization via surrogate random design
SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm
Uncertainty Aware Semi-Supervised Learning on Graph Data
Calibrated Reliable Regression using Maximum Mean Discrepancy
Energy-based Out-of-distribution Detection
Neural Manifold Ordinary Differential Equations
NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity
Adaptation Properties Allow Identification of Optimized Neural Codes
Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice
Learning to search efficiently for causally near-optimal treatments
General Control Functions for Causal Effect Estimation from IVs
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe
Active Structure Learning of Causal DAGs via Directed Clique Trees
Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach
Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method
Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions
Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
Batched Coarse Ranking in Multi-Armed Bandits
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs
Stage-wise Conservative Linear Bandits
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring
Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits
Delay and Cooperation in Nonstochastic Linear Bandits
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
Dynamic Regret of Convex and Smooth Functions
Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition
Online learning with dynamics: A minimax perspective
A Tight Lower Bound and Efficient Reduction for Swap Regret
Making Non-Stochastic Control (Almost) as Easy as Stochastic
Non-Stochastic Control with Bandit Feedback
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence
Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels
Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction
Generative 3D Part Assembly via Dynamic Graph Learning
Online Adaptation for Consistent Mesh Reconstruction in the Wild
Learning Deformable Tetrahedral Meshes for 3D Reconstruction
Weakly Supervised Deep Functional Maps for Shape Matching
Neural Star Domain as Primitive Representation
Robust Multi-Object Matching via Iterative Reweighting of the Graph Connection Laplacian
Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps
CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations
ShapeFlow: Learnable Deformation Flows Among 3D Shapes
Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks
Texture Interpolation for Probing Visual Perception
Byzantine Resilient Distributed Multi-Task Learning
FrugalML: How to use ML Prediction APIs more accurately and cheaply
Improving Auto-Augment via Augmentation-Wise Weight Sharing
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
RandAugment: Practical Automated Data Augmentation with a Reduced Search Space
Meta-Neighborhoods
A Closer Look at the Training Strategy for Modern Meta-Learning
Transferable Calibration with Lower Bias and Variance in Domain Adaptation
Unsupervised Learning of Object Landmarks via Self-Training Correspondence
Autofocused oracles for model-based design
Hierarchical Neural Architecture Search for Deep Stereo Matching
UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection
Hierarchical Granularity Transfer Learning
PyGlove: Symbolic Programming for Automated Machine Learning
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning
Distributed Distillation for On-Device Learning
(ends 11:00 PM)
THU 10 DEC
midnight
Tutorial:
(Track2) Deep Conversational AI Q&A
(ends 12:50 AM)
2 a.m.
Tutorial:
(Track3) Designing Learning Dynamics Q&A
(ends 2:50 AM)
3 a.m.
Tutorial:
(Track3) Deep Implicit Layers: Neural ODEs, Equilibrium Models, and Differentiable Optimization Q&A
(ends 3:50 AM)
4 a.m.
Town Hall:
NeurIPS Town Hall
(duration 1.0 hr)
5 a.m.
Invited Talk (Breiman Lecture):
Causal Learning
Marloes Maathuis
(ends 7:00 AM)
6 a.m.
Orals & Spotlights Track 25: Probabilistic Models/Statistics [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification
[6:15] Fast and Flexible Temporal Point Processes with Triangular Maps
[6:30] Greedy inference with structure-exploiting lazy maps
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Sampling from a k-DPP without looking at all items
[7:10] Non-parametric Models for Non-negative Functions
[7:20] Distribution-free binary classification: prediction sets, confidence intervals and calibration
[7:30] Factor Graph Grammars
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:40
[7:50] Asymptotically Optimal Exact Minibatch Metropolis-Hastings
[8:00] Bayes Consistency vs. H-Consistency: The Interplay between Surrogate Loss Functions and the Scoring Function Class
[8:10] Confidence sequences for sampling without replacement
[8:20] Statistical and Topological Properties of Sliced Probability Divergences
[8:30] Testing Determinantal Point Processes
Q&A s 8:40-8:50
[8:40] Joint Q&A for Preceeding Spotlights
Break s 8:50-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 26: Graph/Relational/Theory [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Graph Cross Networks with Vertex Infomax Pooling
[6:15] Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
[6:30] Graph Random Neural Networks for Semi-Supervised Learning on Graphs
Break s 6:45-7:00
Break
Spotlight s 7:00-7:20
[7:00] Learning Graph Structure With A Finite-State Automaton Layer
[7:10] Pointer Graph Networks
Q&A s 7:20-7:30
[7:20] Joint Q&A for Preceeding Spotlights
Spotlight s 7:30-7:40
[7:30] Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
[8:00] Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
[8:10] Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations
[8:20] Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 27: Unsupervised/Probabilistic [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Contrastive learning of global and local features for medical image segmentation with limited annotations
[6:15] Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning
[6:30] SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Self-Supervised Relational Reasoning for Representation Learning
[7:10] Object-Centric Learning with Slot Attention
[7:20] Telescoping Density-Ratio Estimation
[7:30] Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks
[8:00] Stochastic Normalizing Flows
[8:10] Generative Neurosymbolic Machines
[8:20] DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 28: Deep Learning [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] A shooting formulation of deep learning
[6:15] On the training dynamics of deep networks with $L_2$ regularization
[6:30] Compositional Explanations of Neurons
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints
[7:10] On Power Laws in Deep Ensembles
[7:20] Learning the Geometry of Wave-Based Imaging
[7:30] The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Sparse and Continuous Attention Mechanisms
[8:00] Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks
[8:10] Directional convergence and alignment in deep learning
[8:20] Neural Controlled Differential Equations for Irregular Time Series
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 29: Neuroscience [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Learning abstract structure for drawing by efficient motor program induction
[6:15] Non-reversible Gaussian processes for identifying latent dynamical structure in neural data
[6:30] Gibbs Sampling with People
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Stable and expressive recurrent vision models
[7:10] Identifying Learning Rules From Neural Network Observables
[7:20] A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons
[7:30] Modeling Shared responses in Neuroimaging Studies through MultiView ICA
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning​
[8:00] Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
[8:10] System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina
[8:20] A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 30: Optimization/Theory [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
[6:15] Towards a Better Global Loss Landscape of GANs
[6:30] Online Sinkhorn: Optimal Transport distances from sample streams
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
[7:10] Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions
[7:20] Conformal Symplectic and Relativistic Optimization
[7:30] Random Reshuffling is Not Always Better
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] The Statistical Complexity of Early-Stopped Mirror Descent
[8:00] Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree
[8:10] Towards Problem-dependent Optimal Learning Rates
[8:20] On Uniform Convergence and Low-Norm Interpolation Learning
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 AM)
Orals & Spotlights Track 31: Reinforcement Learning [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs
[6:15] Self-Paced Deep Reinforcement Learning
[6:30] Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Bandit Linear Control
[7:10] Neural Dynamic Policies for End-to-End Sensorimotor Learning
[7:20] Effective Diversity in Population Based Reinforcement Learning
[7:30] Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Reward Propagation Using Graph Convolutional Networks
[8:00] On the Convergence of Smooth Regularized Approximate Value Iteration Schemes
[8:10] Latent World Models For Intrinsically Motivated Exploration
[8:20] Learning to Play No-Press Diplomacy with Best Response Policy Iteration
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 AM)
9 a.m.
Poster Session 6 [9:00-11:00]
Unsupervised Sound Separation Using Mixture Invariant Training
Neural Networks with Small Weights and Depth-Separation Barriers
The Implications of Local Correlation on Learning Some Deep Functions
Provably adaptive reinforcement learning in metric spaces
Instance Based Approximations to Profile Maximum Likelihood
Continual Learning of Control Primitives : Skill Discovery via Reset-Games
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping
Transductive Information Maximization for Few-Shot Learning
Meta-Learning Requires Meta-Augmentation
Co-Tuning for Transfer Learning
What Makes for Good Views for Contrastive Learning?
Self-Supervised Relational Reasoning for Representation Learning
Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning
Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning
Supervised Contrastive Learning
Curriculum Learning by Dynamic Instance Hardness
SuperLoss: A Generic Loss for Robust Curriculum Learning
Neural Topographic Factor Analysis for fMRI Data
Self-supervised learning through the eyes of a child
Learnability with Indirect Supervision Signals
Learning from Label Proportions: A Mutual Contamination Framework
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE
Identifying signal and noise structure in neural population activity with Gaussian process factor models
Neuronal Gaussian Process Regression
Estimating Fluctuations in Neural Representations of Uncertain Environments
Understanding spiking networks through convex optimization
Factorized Neural Processes for Neural Processes: K-Shot Prediction of Neural Responses
Efficient estimation of neural tuning during naturalistic behavior
A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons
Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data
Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons
Predictive coding in balanced neural networks with noise, chaos and delays
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
Online Neural Connectivity Estimation with Noisy Group Testing
Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations
Rescuing neural spike train models from bad MLE
Flows for simultaneous manifold learning and density estimation
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
User-Dependent Neural Sequence Models for Continuous-Time Event Data
Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models
Hierarchical Quantized Autoencoders
Riemannian Continuous Normalizing Flows
Efficient Learning of Generative Models via Finite-Difference Score Matching
Learning Latent Space Energy-Based Prior Model
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
Stochastic Normalizing Flows
Generative Neurosymbolic Machines
Fast and Flexible Temporal Point Processes with Triangular Maps
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting
Neural Dynamic Policies for End-to-End Sensorimotor Learning
Critic Regularized Regression
Off-Policy Imitation Learning from Observations
Deep Inverse Q-learning with Constraints
Value-driven Hindsight Modelling
Effective Diversity in Population Based Reinforcement Learning
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Reward Propagation Using Graph Convolutional Networks
PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals
Self-Paced Deep Reinforcement Learning
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping
f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning
Strictly Batch Imitation Learning by Energy-based Distribution Matching
Meta-Gradient Reinforcement Learning with an Objective Discovered Online
Inverse Reinforcement Learning from a Gradient-based Learner
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning
Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model
Exponential ergodicity of mirror-Langevin diffusions
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence
Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits
The Statistical Complexity of Early-Stopped Mirror Descent
Statistical and Topological Properties of Sliced Probability Divergences
Sharper Generalization Bounds for Pairwise Learning
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study
Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance
Towards Problem-dependent Optimal Learning Rates
On Uniform Convergence and Low-Norm Interpolation Learning
Estimating weighted areas under the ROC curve
Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization
On Second Order Behaviour in Augmented Neural ODEs
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints
Object-Centric Learning with Slot Attention
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
Probabilistic Time Series Forecasting with Shape and Temporal Diversity
A shooting formulation of deep learning
Training Linear Finite-State Machines
Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations
Fast Transformers with Clustered Attention
SMYRF - Efficient Attention using Asymmetric Clustering
Sparse and Continuous Attention Mechanisms
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Denoising Diffusion Probabilistic Models
Untangling tradeoffs between recurrence and self-attention in artificial neural networks
Neural Controlled Differential Equations for Irregular Time Series
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models
Pointer Graph Networks
Can Graph Neural Networks Count Substructures?
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
Graph Contrastive Learning with Augmentations
Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks
Graph Stochastic Neural Networks for Semi-supervised Learning
DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection
Curvature Regularization to Prevent Distortion in Graph Embedding
GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs
Subgraph Neural Networks
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks
Factor Graph Neural Networks
WOR and $p$'s: Sketches for $\ell_p$-Sampling Without Replacement
Statistical Guarantees of Distributed Nearest Neighbor Classification
Robust Persistence Diagrams using Reproducing Kernels
Regression with reject option and application to kNN
A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning
Uncertainty Quantification for Inferring Hawkes Networks
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples
Confidence sequences for sampling without replacement
Truthful Data Acquisition via Peer Prediction
Axioms for Learning from Pairwise Comparisons
Testing Determinantal Point Processes
Coded Sequential Matrix Multiplication For Straggler Mitigation
Functional Regularization for Representation Learning: A Unified Theoretical Perspective
Adaptive Sampling for Stochastic Risk-Averse Learning
High-Dimensional Sparse Linear Bandits
Adversarial Bandits with Corruptions
On Regret with Multiple Best Arms
Model Selection in Contextual Stochastic Bandit Problems
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits
Adapting to Misspecification in Contextual Bandits
Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms
Bandit Linear Control
Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs
On the Convergence of Smooth Regularized Approximate Value Iteration Schemes
Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning
Implicit Distributional Reinforcement Learning
Small Nash Equilibrium Certificates in Very Large Games
Contextual Games: Multi-Agent Learning with Side Information
Recursive Inference for Variational Autoencoders
Bayesian Pseudocoresets
Variational Bayesian Monte Carlo with Noisy Likelihoods
Bayesian Probabilistic Numerical Integration with Tree-Based Models
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks
Decentralized Langevin Dynamics for Bayesian Learning
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond
Decision-Making with Auto-Encoding Variational Bayes
Robust, Accurate Stochastic Optimization for Variational Inference
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization
Efficient Low Rank Gaussian Variational Inference for Neural Networks
Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations
Markovian Score Climbing: Variational Inference with KL(p||q)
Projected Stein Variational Gradient Descent
Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing
SnapBoost: A Heterogeneous Boosting Machine
The Wasserstein Proximal Gradient Algorithm
Unbalanced Sobolev Descent
Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction
Multiparameter Persistence Image for Topological Machine Learning
Learning with Differentiable Pertubed Optimizers
Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions
Learning outside the Black-Box: The pursuit of interpretable models
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization
Random Reshuffling is Not Always Better
Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters
Online Sinkhorn: Optimal Transport distances from sample streams
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence
Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting
The NetHack Learning Environment
Discovering Reinforcement Learning Algorithms
Latent World Models For Intrinsically Motivated Exploration
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Learning to Incentivize Other Learning Agents
Calibration of Shared Equilibria in General Sum Partially Observable Markov Games
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning
Learning to Play No-Press Diplomacy with Best Response Policy Iteration
A Boolean Task Algebra for Reinforcement Learning
Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control
Munchausen Reinforcement Learning
Information-theoretic Task Selection for Meta-Reinforcement Learning
Automatic Curriculum Learning through Value Disagreement
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula
Learning to Prove Theorems by Learning to Generate Theorems
CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation
Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations
Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms.
Taming Discrete Integration via the Boon of Dimensionality
Belief Propagation Neural Networks
Scalable Belief Propagation via Relaxed Scheduling
Probabilistic Circuits for Variational Inference in Discrete Graphical Models
Towards Scalable Bayesian Learning of Causal DAGs
A Novel Approach for Constrained Optimization in Graphical Models
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks
Factor Graph Grammars
Efficient Learning of Discrete Graphical Models
Online Bayesian Goal Inference for Boundedly Rational Planning Agents
Greedy inference with structure-exploiting lazy maps
Biologically Inspired Mechanisms for Adversarial Robustness
Bayes Consistency vs. H-Consistency: The Interplay between Surrogate Loss Functions and the Scoring Function Class
Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
A Unified View of Label Shift Estimation
Calibrating Deep Neural Networks using Focal Loss
Distribution-free binary classification: prediction sets, confidence intervals and calibration
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling
Exchangeable Neural ODE for Set Modeling
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
Further Analysis of Outlier Detection with Deep Generative Models
Sample Complexity of Uniform Convergence for Multicalibration
Certifiably Adversarially Robust Detection of Out-of-Distribution Data
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples
Reducing Adversarially Robust Learning to Non-Robust PAC Learning
Tensor Completion Made Practical
Online Matrix Completion with Side Information
Truncated Linear Regression in High Dimensions
Finer Metagenomic Reconstruction via Biodiversity Optimization
Implicit Regularization in Deep Learning May Not Be Explainable by Norms
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples
Towards a Better Global Loss Landscape of GANs
Implicit Regularization and Convergence for Weight Normalization
Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations
Geometric Exploration for Online Control
The Smoothed Possibility of Social Choice
Optimally Deceiving a Learning Leader in Stackelberg Games
Explainable Voting
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions
Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree
A Benchmark for Systematic Generalization in Grounded Language Understanding
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks
Meta-Learning through Hebbian Plasticity in Random Networks
Stable and expressive recurrent vision models
Identifying Learning Rules From Neural Network Observables
Deep active inference agents using Monte-Carlo methods
Learning Compositional Rules via Neural Program Synthesis
A Local Temporal Difference Code for Distributional Reinforcement Learning
Inferring learning rules from animal decision-making
A Biologically Plausible Neural Network for Slow Feature Analysis
R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking
Characterizing emergent representations in a space of candidate learning rules for deep networks
A simple normative network approximates local non-Hebbian learning in the cortex
A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network
Detection as Regression: Certified Object Detection with Median Smoothing
Certifying Confidence via Randomized Smoothing
Reliable Graph Neural Networks via Robust Aggregation
PLLay: Efficient Topological Layer based on Persistent Landscapes
Network Diffusions via Neural Mean-Field Dynamics
Learning the Geometry of Wave-Based Imaging
Neuron Shapley: Discovering the Responsible Neurons
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks
Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth
Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
Discovering Symbolic Models from Deep Learning with Inductive Biases
Compositional Explanations of Neurons
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality
Telescoping Density-Ratio Estimation
On Testing of Samplers
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks
Deep Direct Likelihood Knockoffs
Generalised Bayesian Filtering via Sequential Monte Carlo
Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests
Bayesian Optimization of Risk Measures
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
Variance reduction for Random Coordinate Descent-Langevin Monte Carlo
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions
Sequential Bayesian Experimental Design with Variable Cost Structure
Asymptotically Optimal Exact Minibatch Metropolis-Hastings
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm
Replica-Exchange Nos\'e-Hoover Dynamics for Bayesian Learning on Large Datasets
Stein Self-Repulsive Dynamics: Benefits From Past Samples
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning
Bootstrapping neural processes
Sparse Learning with CART
Smooth And Consistent Probabilistic Regression Trees
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Bayesian Deep Ensembles via the Neural Tangent Kernel
Predictive inference is free with the jackknife+-after-bootstrap
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Depth Uncertainty in Neural Networks
A Bayesian Perspective on Training Speed and Model Selection
Learning under Model Misspecification: Applications to Variational and Ensemble methods
Counterfactual Predictions under Runtime Confounding
Matérn Gaussian Processes on Riemannian Manifolds
Stationary Activations for Uncertainty Calibration in Deep Learning
Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters
On the Expressiveness of Approximate Inference in Bayesian Neural Networks
The Strong Screening Rule for SLOPE
Co-exposure Maximization in Online Social Networks
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev
Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control
MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics
Semialgebraic Optimization for Lipschitz Constants of ReLU Networks
Parabolic Approximation Line Search for DNNs
Batch normalization provably avoids ranks collapse for randomly initialised deep networks
Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices
A mean-field analysis of two-player zero-sum games
Robust Federated Learning: The Case of Affine Distribution Shifts
Learning compositional functions via multiplicative weight updates
Stochastic Optimization for Performative Prediction
Conformal Symplectic and Relativistic Optimization
On Power Laws in Deep Ensembles
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
Self-Distillation as Instance-Specific Label Smoothing
On the training dynamics of deep networks with $L_2$ regularization
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate
Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning
Bad Global Minima Exist and SGD Can Reach Them
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks
Ensemble Distillation for Robust Model Fusion in Federated Learning
On Warm-Starting Neural Network Training
Predicting Training Time Without Training
Directional convergence and alignment in deep learning
Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
Consistent feature selection for analytic deep neural networks
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms
Sampling from a k-DPP without looking at all items
Optimal Learning from Verified Training Data
Empirical Likelihood for Contextual Bandits
Sufficient dimension reduction for classification using principal optimal transport direction
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
Listening to Sounds of Silence for Speech Denoising
A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction
Geometric All-way Boolean Tensor Decomposition
A novel variational form of the Schatten-$p$ quasi-norm
Distributionally Robust Parametric Maximum Likelihood Estimation
Adaptive Probing Policies for Shortest Path Routing
Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions
Content Provider Dynamics and Coordination in Recommendation Ecosystems
Non-parametric Models for Non-negative Functions
Program Synthesis with Pragmatic Communication
Detecting Interactions from Neural Networks via Topological Analysis
Learning efficient task-dependent representations with synaptic plasticity
Modeling Shared responses in Neuroimaging Studies through MultiView ICA
Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning​
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech
Learning abstract structure for drawing by efficient motor program induction
Mutual exclusivity as a challenge for deep neural networks
Gibbs Sampling with People
Learning sparse codes from compressed representations with biologically plausible local wiring constraints
Shared Space Transfer Learning for analyzing multi-site fMRI data
Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction
System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina
An Unsupervised Information-Theoretic Perceptual Quality Metric
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
CoMIR: Contrastive Multimodal Image Representation for Registration
CrossTransformers: spatially-aware few-shot transfer
Contrastive learning of global and local features for medical image segmentation with limited annotations
3D Self-Supervised Methods for Medical Imaging
Unsupervised Learning of Dense Visual Representations
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases
Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
LoCo: Local Contrastive Representation Learning
Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization
Differentiable Augmentation for Data-Efficient GAN Training
Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection
ContraGAN: Contrastive Learning for Conditional Image Generation
Inverting Gradients - How easy is it to break privacy in federated learning?
Principal Neighbourhood Aggregation for Graph Nets
Learning Graph Structure With A Finite-State Automaton Layer
Graph Cross Networks with Vertex Infomax Pooling
How hard is to distinguish graphs with graph neural networks?
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
COPT: Coordinated Optimal Transport on Graphs
Building powerful and equivariant graph neural networks with structural message-passing
Rethinking pooling in graph neural networks
Random Walk Graph Neural Networks
Path Integral Based Convolution and Pooling for Graph Neural Networks
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Graphon Neural Networks and the Transferability of Graph Neural Networks
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks
Parameterized Explainer for Graph Neural Network
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning
(ends 11:00 AM)
noon
Affinity Workshop:
Indigenous in AI
(ends 2:10 PM)
Tutorial:
(Track1) Federated Learning and Analytics: Industry Meets Academia Q&A
(ends 12:50 PM)
1 p.m.
Tutorial:
(Track3) Policy Optimization in Reinforcement Learning Q&A
(ends 1:50 PM)
4 p.m.
Town Hall:
NeurIPS Town Hall
(duration 1.0 hr)
5 p.m.
Invited Talk:
The Genomic Bottleneck: A Lesson from Biology
Anthony M Zador
(ends 7:00 PM)
6 p.m.
Orals & Spotlights Track 32: Optimization [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization
[6:15] The Primal-Dual method for Learning Augmented Algorithms
[6:30] Fully Dynamic Algorithm for Constrained Submodular Optimization
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Submodular Maximization Through Barrier Functions
[7:10] Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method
[7:20] A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints
[7:30] How many samples is a good initial point worth in Low-rank Matrix Recovery?
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Projection Robust Wasserstein Distance and Riemannian Optimization
[8:00] A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval
[8:10] SGD with shuffling: optimal rates without component convexity and large epoch requirements
[8:20] No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 PM)
Orals & Spotlights Track 33: Health/AutoML/(Soft|Hard)ware [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Theory-Inspired Path-Regularized Differential Network Architecture Search
[6:15] Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows
[6:30] Transferable Graph Optimizers for ML Compilers
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] A Study on Encodings for Neural Architecture Search
[7:10] Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding
[7:20] Evolving Normalization-Activation Layers
[7:30] Open Graph Benchmark: Datasets for Machine Learning on Graphs
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:20
[7:50] Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning
[8:00] MCUNet: Tiny Deep Learning on IoT Devices
[8:10] Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming
Q&A s 8:20-8:30
[8:20] Joint Q&A for Preceeding Spotlights
Break s 8:30-9:00
Break
(ends 9:00 PM)
Orals & Spotlights Track 34: Deep Learning [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Is normalization indispensable for training deep neural network?
[6:15] Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks
[6:30] Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics
[7:10] Kernel Based Progressive Distillation for Adder Neural Networks
[7:20] What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
[7:30] Collegial Ensembles
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:30
[7:50] Finite Versus Infinite Neural Networks: an Empirical Study
[8:00] Estimating Training Data Influence by Tracing Gradient Descent
[8:10] AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
[8:20] Part-dependent Label Noise: Towards Instance-dependent Label Noise
Q&A s 8:30-8:40
[8:30] Joint Q&A for Preceeding Spotlights
Break s 8:40-9:00
Break
(ends 9:00 PM)
Orals & Spotlights Track 35: Neuroscience/Probabilistic [6:00-9:00]
Each Oral includes Q&A
Spotlights have joint Q&As
Oral s 6:00-6:45
[6:00] Point process models for sequence detection in high-dimensional neural spike trains
[6:15] Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN
[6:30] A mathematical theory of cooperative communication
Break s 6:45-7:00
Break
Spotlight s 7:00-7:40
[7:00] Learning Some Popular Gaussian Graphical Models without Condition Number Bounds
[7:10] Sinkhorn Natural Gradient for Generative Models
[7:20] NVAE: A Deep Hierarchical Variational Autoencoder
[7:30] Reciprocal Adversarial Learning via Characteristic Functions
Q&A s 7:40-7:50
[7:40] Joint Q&A for Preceeding Spotlights
Spotlight s 7:50-8:20
[7:50] Incorporating Interpretable Output Constraints in Bayesian Neural Networks
[8:00] Baxter Permutation Process
[8:10] Flexible mean field variational inference using mixtures of non-overlapping exponential families
Q&A s 8:20-8:30
[8:20] Joint Q&A for Preceeding Spotlights
Break s 8:30-9:00
Break
(ends 9:00 PM)
9 p.m.
Poster Session 7 [9:00-11:00]
Linear-Sample Learning of Low-Rank Distributions
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification
Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?
Generalization Bound of Gradient Descent for Non-Convex Metric Learning
On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression
Learning to Approximate a Bregman Divergence
Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel
Learning to solve TV regularised problems with unrolled algorithms
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method
Neural Networks Learning and Memorization with (almost) no Over-Parameterization
From Boltzmann Machines to Neural Networks and Back Again
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm
Projection Robust Wasserstein Distance and Riemannian Optimization
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View
Minimax Bounds for Generalized Linear Models
Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics
Deep reconstruction of strange attractors from time series
STEER : Simple Temporal Regularization For Neural ODE
Learning Manifold Implicitly via Explicit Heat-Kernel Learning
Better Set Representations For Relational Reasoning
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks
Model Inversion Networks for Model-Based Optimization
Variational Amodal Object Completion
Low Distortion Block-Resampling with Spatially Stochastic Networks
Understanding Deep Architecture with Reasoning Layer
AdaTune: Adaptive Tensor Program Compilation Made Efficient
CircleGAN: Generative Adversarial Learning across Spherical Circles
SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds
Improved Techniques for Training Score-Based Generative Models
UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging
Deep Archimedean Copulas
Constraining Variational Inference with Geometric Jensen-Shannon Divergence
CO-Optimal Transport
OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming
Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows
HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory
Noise-Contrastive Estimation for Multivariate Point Processes
Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier with Application to Real-Time Information Filtering on the Web
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning
Baxter Permutation Process
A mathematical theory of cooperative communication
All your loss are belong to Bayes
The Potts-Ising model for discrete multivariate data
Bidirectional Convolutional Poisson Gamma Dynamical Systems
Variational Bayesian Unlearning
Theory-Inspired Path-Regularized Differential Network Architecture Search
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search
Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement
AutoBSS: An Efficient Algorithm for Block Stacking Style Search
Semi-Supervised Neural Architecture Search
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?
A Study on Encodings for Neural Architecture Search
Evolving Normalization-Activation Layers
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding
Auto Learning Attention
Transferable Graph Optimizers for ML Compilers
Adapting Neural Architectures Between Domains
Revisiting Parameter Sharing for Automatic Neural Channel Number Search
Neuron-level Structured Pruning using Polarization Regularizer
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
MCUNet: Tiny Deep Learning on IoT Devices
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding
Bi-level Score Matching for Learning Energy-based Latent Variable Models
NVAE: A Deep Hierarchical Variational Autoencoder
Reciprocal Adversarial Learning via Characteristic Functions
Stochastic Stein Discrepancies
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights
Incorporating Interpretable Output Constraints in Bayesian Neural Networks
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables
Walsh-Hadamard Variational Inference for Bayesian Deep Learning
f-Divergence Variational Inference
Flexible mean field variational inference using mixtures of non-overlapping exponential families
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method
Improving Online Rent-or-Buy Algorithms with Sequential Decision Making and ML Predictions
Community detection using fast low-cardinality semidefinite programming

Online Optimization with Memory and Competitive Control
Simple and Fast Algorithm for Binary Integer and Online Linear Programming
A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints
Online Convex Optimization Over Erdos-Renyi Random Networks
Thunder: a Fast Coordinate Selection Solver for Sparse Learning
Deterministic Approximation for Submodular Maximization over a Matroid in Nearly Linear Time
The Primal-Dual method for Learning Augmented Algorithms
Fully Dynamic Algorithm for Constrained Submodular Optimization
Submodular Maximization Through Barrier Functions
Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds
Robust Sequence Submodular Maximization
Continuous Submodular Maximization: Beyond DR-Submodularity
Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent
Towards More Practical Adversarial Attacks on Graph Neural Networks
Boundary thickness and robustness in learning models
Exploiting weakly supervised visual patterns to learn from partial annotations
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
Part-dependent Label Noise: Towards Instance-dependent Label Noise
Digraph Inception Convolutional Networks
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
Self-Adaptive Training: beyond Empirical Risk Minimization
Debugging Tests for Model Explanations
Point process models for sequence detection in high-dimensional neural spike trains
Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks
Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN
High-contrast “gaudy” images improve the training of deep neural network models of visual cortex
Weakly-Supervised Reinforcement Learning for Controllable Behavior
Predictive Information Accelerates Learning in RL
The route to chaos in routing games: When is price of anarchy too optimistic?
Graph Meta Learning via Local Subgraphs
Graph Information Bottleneck
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs
Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding
Scalable Graph Neural Networks via Bidirectional Propagation
Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs
A graph similarity for deep learning
Implicit Graph Neural Networks
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
Disentangling Human Error from Ground Truth in Segmentation of Medical Images
Graph Policy Network for Transferable Active Learning on Graphs
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Factorizable Graph Convolutional Networks
Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning
Natural Graph Networks
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian
Sinkhorn Natural Gradient for Generative Models
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model
How many samples is a good initial point worth in Low-rank Matrix Recovery?
Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization
Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization
Tight last-iterate convergence rates for no-regret learning in multi-player games
A General Large Neighborhood Search Framework for Solving Integer Linear Programs
Sinkhorn Barycenter via Functional Gradient Descent
Improved Analysis of Clipping Algorithms for Non-convex Optimization
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval
Federated Accelerated Stochastic Gradient Descent
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
SGD with shuffling: optimal rates without component convexity and large epoch requirements
No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix
Generalized Leverage Score Sampling for Neural Networks
Passport-aware Normalization for Deep Model Protection
Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets
Neural Networks Fail to Learn Periodic Functions and How to Fix It
Finite Versus Infinite Neural Networks: an Empirical Study
Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks
Collegial Ensembles
Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples
Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians
Coresets for Robust Training of Deep Neural Networks against Noisy Labels
Learning Deep Attribution Priors Based On Prior Knowledge
Estimating Training Data Influence by Tracing Gradient Descent
Escaping Saddle-Point Faster under Interpolation-like Conditions
Learning Loss for Test-Time Augmentation
Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles
The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification
Is normalization indispensable for training deep neural network?
SCOP: Scientific Control for Reliable Neural Network Pruning
Train-by-Reconnect: Decoupling Locations of Weights from Their Values
Deep Metric Learning with Spherical Embedding
Kernel Based Progressive Distillation for Adder Neural Networks
Top-KAST: Top-K Always Sparse Training
Task-Oriented Feature Distillation
Rotated Binary Neural Network
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space
Sparse Weight Activation Training
Recurrent Quantum Neural Networks
Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee
(ends 11:00 PM)
FRI 11 DEC
midnight
Workshops Fri [12:00-2:59]
Workshop s 11:00-4:00
Topological Data Analysis and Beyond
Privacy Preserving Machine Learning - PriML and PPML Joint Edition
Tackling Climate Change with ML
Meta-Learning
OPT2020: Optimization for Machine Learning
Advances and Opportunities: Machine Learning for Education
Differential Geometry meets Deep Learning (DiffGeo4DL)
Workshop on Dataset Curation and Security
Machine Learning for Health (ML4H): Advancing Healthcare for All
Learning Meaningful Representations of Life (LMRL.org)
First Workshop on Quantum Tensor Networks in Machine Learning
Human in the loop dialogue systems
The pre-registration experiment: an alternative publication model for machine learning research
Differentiable computer vision, graphics, and physics in machine learning
Self-Supervised Learning for Speech and Audio Processing
Causal Discovery and Causality-Inspired Machine Learning
Machine Learning and the Physical Sciences
Resistance AI Workshop
ML Competitions at the Grassroots (CiML 2020)
3rd Robot Learning Workshop
Workshop on Deep Learning and Inverse Problems
Machine Learning for Autonomous Driving
Competition Track Friday
Object Representations for Learning and Reasoning
Fair AI in Finance
Crowd Science Workshop: Remoteness, Fairness, and Mechanisms as Challenges of Data Supply by Humans for Automation
ML Retrospectives, Surveys & Meta-Analyses (ML-RSA)
Deep Reinforcement Learning
KR2ML - Knowledge Representation and Reasoning Meets Machine Learning
BabyMind: How Babies Learn and How Machines Can Imitate
Machine Learning for Economic Policy
(ends 2:59 PM)
4 p.m.
Workshops Sat [4:00-3:59]
Workshop s 1:00-6:30
Algorithmic Fairness through the Lens of Causality and Interpretability
Medical Imaging Meets NeurIPS
Learning Meets Combinatorial Algorithms
Machine Learning for the Developing World (ML4D): Improving Resilience
Biological and Artificial Reinforcement Learning
I Can’t Believe It’s Not Better! Bridging the gap between theory and empiricism in probabilistic machine learning
Machine Learning for Engineering Modeling, Simulation and Design
Machine Learning for Creativity and Design 4.0
Cooperative AI
Navigating the Broader Impacts of AI Research
Machine Learning for Molecules
Beyond BackPropagation: Novel Ideas for Training Neural Architectures
Wordplay: When Language Meets Games
MLPH: Machine Learning in Public Health
Interpretable Inductive Biases and Physically Structured Learning
AI for Earth Sciences
Talking to Strangers: Zero-Shot Emergent Communication
Machine Learning for Mobile Health
Shared Visual Representations in Human and Machine Intelligence (SVRHM)
Second Workshop on AI for Humanitarian Assistance and Disaster Response
Consequential Decisions in Dynamic Environments
Competition Track Saturday
Machine Learning for Structural Biology
HAMLETS: Human And Model in the Loop Evaluation and Training Strategies
International Workshop on Scalability, Privacy, and Security in Federated Learning (SpicyFL 2020)
Workshop on Computer Assisted Programming (CAP)
The Challenges of Real World Reinforcement Learning
Self-Supervised Learning -- Theory and Practice
Offline Reinforcement Learning
Machine Learning for Systems
Deep Learning through Information Geometry
(ends 3:59 PM)