NIPS 2018 Events with Videos

Breaks

• Inclusion and Diversity Town Hall

Invited Talks

• Accountability and Algorithmic Bias: Why Diversity and Inclusion Matters
• Machine Learning Meets Public Policy: What to Expect and How to Cope
• What Bodies Think About: Bioelectric Computation Outside the Nervous System, Primitive Cognition, and Synthetic Morphology
• Investigations into the Human-AI Trust Phenomenon
• Designing Computer Systems for Software 2.0

Invited Talk (Breiman Lecture)s

• Making Algorithms Trustworthy: What Can Statistical Science Contribute to Transparency, Explanation and Validation?

Invited Talk (Posner Lecture)s

• Reproducible, Reusable, and Robust Reinforcement Learning

Orals

• On Neuronal Capacity
• Phase Retrieval Under a Generative Prior
• Dendritic cortical microcircuits approximate the backpropagation algorithm
• Spectral Filtering for General Linear Dynamical Systems
• Generalisation of structural knowledge in the hippocampal-entorhinal system
• Neural Ordinary Differential Equations
• Model-Agnostic Private Learning
• A probabilistic population code based on neural samples
• How Does Batch Normalization Help Optimization?
• Learning to Solve SMT Formulas
• Exploration in Structured Reinforcement Learning
• Visual Memory for Robust Path Following
• Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes
• Policy Optimization via Importance Sampling
• Isolating Sources of Disentanglement in Variational Autoencoders
• Stochastic Cubic Regularization for Fast Nonconvex Optimization
• Recurrent World Models Facilitate Policy Evolution
• Approximate Knowledge Compilation by Online Collapsed Importance Sampling
• Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems
• Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models
• Variational Inference with Tail-adaptive f-Divergence
• Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
• Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
• Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
• Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
• Non-delusional Q-learning and value-iteration
• Learning to Reconstruct Shapes from Unseen Classes
• Smoothed analysis of the low-rank approach for smooth semidefinite programs

Posters

• Memory Replay GANs: Learning to Generate New Categories without Forgetting
• Knowledge Distillation by On-the-Fly Native Ensemble
• A Convex Duality Framework for GANs
• SLAYER: Spike Layer Error Reassignment in Time
• Scalable methods for 8-bit training of neural networks
• Expanding Holographic Embeddings for Knowledge Completion
• Middle-Out Decoding
• A Bridging Framework for Model Optimization and Deep Propagation
• Video-to-Video Synthesis
• A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication
• An intriguing failing of convolutional neural networks and the CoordConv solution
• DeepPINK: reproducible feature selection in deep neural networks
• HOUDINI: Lifelong Learning as Program Synthesis
• Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices
• Exact natural gradient in deep linear networks and its application to the nonlinear case
• With Friends Like These, Who Needs Adversaries?
• On the Dimensionality of Word Embedding
• Deep Neural Nets with Interpolating Function as Output Activation
• Predictive Uncertainty Estimation via Prior Networks
• Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres
• Bilinear Attention Networks
• Learning towards Minimum Hyperspherical Energy
• Flexible neural representation for physics prediction
• GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
• Learning Versatile Filters for Efficient Convolutional Neural Networks
• Phase Retrieval Under a Generative Prior
• Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds
• Unsupervised Learning of Artistic Styles with Archetypal Style Analysis
• See and Think: Disentangling Semantic Scene Completion
• Speaker-Follower Models for Vision-and-Language Navigation
• Unsupervised Learning of Shape and Pose with Differentiable Point Clouds
• Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation
• Active Matting
• Neural Tangent Kernel: Convergence and Generalization in Neural Networks
• Ex ante coordination and collusion in zero-sum multi-player extensive-form games
• Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering
• Neural Voice Cloning with a Few Samples
• How Does Batch Normalization Help Optimization?
• BinGAN: Learning Compact Binary Descriptors with a Regularized GAN
• Chain of Reasoning for Visual Question Answering
• Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
• A Spectral View of Adversarially Robust Features
• Geometry Based Data Generation
• Training Deep Neural Networks with 8-bit Floating Point Numbers
• Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders
• Bayesian Inference of Temporal Task Specifications from Demonstrations
• Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
• Deep Generative Markov State Models
• Precision and Recall for Time Series
• Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with $\beta$-Divergences
• Efficient Formal Safety Analysis of Neural Networks
• MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
• Differentially Private Testing of Identity and Closeness of Discrete Distributions
• Learning filter widths of spectral decompositions with wavelets
• The Price of Fair PCA: One Extra dimension
• Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
• A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization
• High Dimensional Linear Regression using Lattice Basis Reduction
• Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo
• Hamiltonian Variational Auto-Encoder
• Delta-encoder: an effective sample synthesis method for few-shot object recognition
• Deepcode: Feedback Codes via Deep Learning
• Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
• Critical initialisation for deep signal propagation in noisy rectifier neural networks
• End-to-End Differentiable Physics for Learning and Control
• Learning Optimal Reserve Price against Non-myopic Bidders
• Practical exact algorithm for trembling-hand equilibrium refinements in games
• Sparsified SGD with Memory
• Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making
• Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model
• Unsupervised Depth Estimation, 3D Face Rotation and Replacement
• Neighbourhood Consensus Networks
• Learning to Decompose and Disentangle Representations for Video Prediction
• Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution
• Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
• Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images
• Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds
• Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs
• A Probabilistic U-Net for Segmentation of Ambiguous Images
• How to Start Training: The Effect of Initialization and Architecture
• Learning Safe Policies with Expert Guidance
• Variational Learning on Aggregate Outputs with Gaussian Processes
• Heterogeneous Multi-output Gaussian Process Prediction
• Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues
• Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces
• Depth-Limited Solving for Imperfect-Information Games
• Long short-term memory and Learning-to-learn in networks of spiking neurons
• Alternating optimization of decision trees, with application to learning sparse oblique trees
• The Physical Systems Behind Optimization Algorithms
• Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced
• How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery?
• Scalable Laplacian K-modes
• Legendre Decomposition for Tensors
• Coordinate Descent with Bandit Sampling
• Blind Deconvolutional Phase Retrieval via Convex Programming
• Estimators for Multivariate Information Measures in General Probability Spaces
• Learning to Play With Intrinsically-Motivated, Self-Aware Agents
• Reducing Network Agnostophobia
• Improving Simple Models with Confidence Profiles
• HOGWILD!-Gibbs can be PanAccurate
• Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization
• Geometrically Coupled Monte Carlo Sampling
• Causal Discovery from Discrete Data using Hidden Compact Representation
• Dynamic Network Model from Partial Observations
• Deep Reinforcement Learning of Marked Temporal Point Processes
• Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
• Reinforcement Learning for Solving the Vehicle Routing Problem
• Scalable Coordinated Exploration in Concurrent Reinforcement Learning
• Transfer of Deep Reactive Policies for MDP Planning
• Randomized Prior Functions for Deep Reinforcement Learning
• Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
• Online Robust Policy Learning in the Presence of Unknown Adversaries
• From Stochastic Planning to Marginal MAP
• Learning to Navigate in Cities Without a Map
• Learning Task Specifications from Demonstrations
• Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning
• Unsupervised Video Object Segmentation for Deep Reinforcement Learning
• Playing hard exploration games by watching YouTube
• Representation Balancing MDPs for Off-policy Policy Evaluation
• Occam's razor is insufficient to infer the preferences of irrational agents
• Hardware Conditioned Policies for Multi-Robot Transfer Learning
• Discretely Relaxing Continuous Variables for tractable Variational Inference
• Infinite-Horizon Gaussian Processes
• Scaling Gaussian Process Regression with Derivatives
• Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
• Incorporating Context into Language Encoding Models for fMRI
• Discrimination-aware Channel Pruning for Deep Neural Networks
• Understanding Batch Normalization
• Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo
• Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages
• Do Less, Get More: Streaming Submodular Maximization with Subsampling
• FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
• Can We Gain More from Orthogonality Regularizations in Training Deep Networks?
• Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
• Visual Object Networks: Image Generation with Disentangled 3D Representations
• Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
• Fast Estimation of Causal Interactions using Wold Processes
• Probabilistic Model-Agnostic Meta-Learning
• SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
• A Bayesian Nonparametric View on Count-Min Sketch
• A Stein variational Newton method
• Deep State Space Models for Unconditional Word Generation
• Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems
• Automating Bayesian optimization with Bayesian optimization
• Entropy and mutual information in models of deep neural networks
• Self-Supervised Generation of Spatial Audio for 360° Video
• Why Is My Classifier Discriminatory?
• Mallows Models for Top-k Lists
• Towards Deep Conversational Recommendations
• The committee machine: Computational to statistical gaps in learning a two-layers neural network
• Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
• Processing of missing data by neural networks
• Lifelong Inverse Reinforcement Learning
• Dual Swap Disentangling
• Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
• Overlapping Clustering Models, and One (class) SVM to Bind Them All
• Learning Loop Invariants for Program Verification
• Query K-means Clustering and the Double Dixie Cup Problem
• ResNet with one-neuron hidden layers is a Universal Approximator
• Understanding Regularized Spectral Clustering via Graph Conductance
• Binary Classification from Positive-Confidence Data
• Efficient Anomaly Detection via Matrix Sketching
• Stochastic Chebyshev Gradient Descent for Spectral Optimization
• Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima
• Representation Learning of Compositional Data
• Multi-Class Learning: From Theory to Algorithm
• Hyperbolic Neural Networks
• Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
• Boosted Sparse and Low-Rank Tensor Regression
• (Probably) Concave Graph Matching
• Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators
• Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
• Wavelet regression and additive models for irregularly spaced data
• A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
• Norm matters: efficient and accurate normalization schemes in deep networks
• SimplE Embedding for Link Prediction in Knowledge Graphs
• Efficient nonmyopic batch active search
• Watch Your Step: Learning Node Embeddings via Graph Attention
• Bandit Learning with Implicit Feedback
• Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation
• Multi-armed Bandits with Compensation
• A Smoother Way to Train Structured Prediction Models
• Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
• Unsupervised Learning of View-invariant Action Representations
• Learning convex bounds for linear quadratic control policy synthesis
• Adaptive Learning with Unknown Information Flows
• Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
• CatBoost: unbiased boosting with categorical features
• Bandit Learning with Positive Externalities

Spotlights

• Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data
• Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces
• Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere
• Size-Noise Tradeoffs in Generative Networks
• Diffusion Maps for Textual Network Embedding
• Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds
• Neural Voice Cloning with a Few Samples
• Evolved Policy Gradients
• Differentially Private Testing of Identity and Closeness of Discrete Distributions
• Answerer in Questioner's Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog
• Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning
• Local Differential Privacy for Evolving Data
• Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
• Bayesian Model-Agnostic Meta-Learning
• Differentially Private k-Means with Constant Multiplicative Error
• Learning to Optimize Tensor Programs
• Probabilistic Neural Programmed Networks for Scene Generation
• A Spectral View of Adversarially Robust Features
• A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
• Bias and Generalization in Deep Generative Models: An Empirical Study
• Bounded-Loss Private Prediction Markets
• Generalizing Tree Probability Estimation via Bayesian Networks
• Robustness of conditional GANs to noisy labels
• cpSGD: Communication-efficient and differentially-private distributed SGD
• Geometry Based Data Generation
• BourGAN: Generative Networks with Metric Embeddings
• Adversarially Robust Generalization Requires More Data
• Point process latent variable models of larval zebrafish behavior
• Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
• Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples
• Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding
• Training Neural Networks Using Features Replay
• Towards Robust Detection of Adversarial Examples
• Learning Temporal Point Processes via Reinforcement Learning
• Step Size Matters in Deep Learning
• Neural Architecture Search with Bayesian Optimisation and Optimal Transport
• Precision and Recall for Time Series
• Neural Tangent Kernel: Convergence and Generalization in Neural Networks
• Data-Driven Clustering via Parameterized Lloyd's Families
• Bayesian Nonparametric Spectral Estimation
• Hierarchical Graph Representation Learning with Differentiable Pooling
• Supervising Unsupervised Learning
• A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
• Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images
• Revisiting $(\epsilon, \gamma, \tau)$-similarity learning for domain adaptation
• Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs
• Delta-encoder: an effective sample synthesis method for few-shot object recognition
• Leveraged volume sampling for linear regression
• End-to-End Differentiable Physics for Learning and Control
• Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language
• Synthesize Policies for Transfer and Adaptation across Tasks and Environments
• Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes
• Neighbourhood Consensus Networks
• Sublinear Time Low-Rank Approximation of Distance Matrices
• Acceleration through Optimistic No-Regret Dynamics
• Recurrent Transformer Networks for Semantic Correspondence
• Minimax Statistical Learning with Wasserstein distances
• On Oracle-Efficient PAC RL with Rich Observations
• Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
• Generalization Bounds for Uniformly Stable Algorithms
• Constant Regret, Generalized Mixability, and Mirror Descent
• Sanity Checks for Saliency Maps
• A loss framework for calibrated anomaly detection
• Efficient Online Portfolio with Logarithmic Regret
• A Probabilistic U-Net for Segmentation of Ambiguous Images
• Sharp Bounds for Generalized Uniformity Testing
• Solving Large Sequential Games with the Excessive Gap Technique
• Virtual Class Enhanced Discriminative Embedding Learning
• Convex Elicitation of Continuous Properties
• Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
• Dynamic Network Model from Partial Observations
• The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal
• Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
• Stochastic Nonparametric Event-Tensor Decomposition
• Contextual Stochastic Block Models
• Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
• On GANs and GMMs
• Entropy Rate Estimation for Markov Chains with Large State Space
• Meta-Reinforcement Learning of Structured Exploration Strategies
• GILBO: One Metric to Measure Them All
• Blind Deconvolutional Phase Retrieval via Convex Programming
• A Bayesian Approach to Generative Adversarial Imitation Learning
• Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization
• Visual Reinforcement Learning with Imagined Goals
• Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features
• On the Local Minima of the Empirical Risk
• Randomized Prior Functions for Deep Reinforcement Learning
• Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
• How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery?
• Playing hard exploration games by watching YouTube
• Adversarially Robust Optimization with Gaussian Processes
• SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator
• Reducing Network Agnostophobia
• DAGs with NO TEARS: Continuous Optimization for Structure Learning
• Natasha 2: Faster Non-Convex Optimization Than SGD
• Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies
• Proximal Graphical Event Models
• Escaping Saddle Points in Constrained Optimization
• Geometrically Coupled Monte Carlo Sampling
• Heterogeneous Multi-output Gaussian Process Prediction
• On Coresets for Logistic Regression
• Scalable Laplacian K-modes
• GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
• Legendre Decomposition for Tensors
• Learning with SGD and Random Features
• Graphical model inference: Sequential Monte Carlo meets deterministic approximations
• Boolean Decision Rules via Column Generation
• KONG: Kernels for ordered-neighborhood graphs
• Boosting Black Box Variational Inference
• Fast greedy algorithms for dictionary selection with generalized sparsity constraints
• Quadrature-based features for kernel approximation
• Discretely Relaxing Continuous Variables for tractable Variational Inference
• Distributed $k$-Clustering for Data with Heavy Noise
• Statistical and Computational Trade-Offs in Kernel K-Means
• Implicit Reparameterization Gradients
• Do Less, Get More: Streaming Submodular Maximization with Subsampling
• Why Is My Classifier Discriminatory?
• Mirrored Langevin Dynamics
• Overlapping Clustering Models, and One (class) SVM to Bind Them All
• Human-in-the-Loop Interpretability Prior
• Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
• Removing the Feature Correlation Effect of Multiplicative Noise
• Link Prediction Based on Graph Neural Networks
• Identification and Estimation of Causal Effects from Dependent Data
• Connectionist Temporal Classification with Maximum Entropy Regularization
• Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
• Causal Inference via Kernel Deviance Measures
• Entropy and mutual information in models of deep neural networks
• Automatic differentiation in ML: Where we are and where we should be going
• Removing Hidden Confounding by Experimental Grounding
• The committee machine: Computational to statistical gaps in learning a two-layers neural network
• Robust Subspace Approximation in a Stream
• Hyperbolic Neural Networks
• A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization
• Efficient nonmyopic batch active search
• Norm matters: efficient and accurate normalization schemes in deep networks
• Stochastic Chebyshev Gradient Descent for Spectral Optimization
• Interactive Structure Learning with Structural Query-by-Committee
• Constructing Fast Network through Deconstruction of Convolution
• LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
• Contour location via entropy reduction leveraging multiple information sources
• A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
• Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
• Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes
• Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction
• Direct Runge-Kutta Discretization Achieves Acceleration
• Learning convex bounds for linear quadratic control policy synthesis
• Learning Loop Invariants for Program Verification
• Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives
• Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning
• DeepProbLog: Neural Probabilistic Logic Programming
• (Probably) Concave Graph Matching
• Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
• Learning to Infer Graphics Programs from Hand-Drawn Images
• Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
• Bilevel learning of the Group Lasso structure
• Improving Neural Program Synthesis with Inferred Execution Traces
• Wasserstein Distributionally Robust Kalman Filtering
• Binary Classification from Positive-Confidence Data
• ResNet with one-neuron hidden layers is a Universal Approximator
• Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
• Fully Understanding The Hashing Trick
• Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation
• Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
• Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds
• Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
• Convergence of Cubic Regularization for Nonconvex Optimization under KL Property

Talks

• Test of Time Award

Tutorials

• Adversarial Robustness: Theory and Practice
• Visualization for Machine Learning
• Scalable Bayesian Inference
• Unsupervised Deep Learning
• Common Pitfalls for Studying the Human Side of Machine Learning
• Negative Dependence, Stable Polynomials, and All That
• Automatic Machine Learning
• Statistical Learning Theory: a Hitchhiker's Guide
• Counterfactual Inference

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