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Learning with Feature Evolvable Streams
Reconstruct & Crush Network
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks
Sobolev Training for Neural Networks
Probabilistic Rule Realization and Selection
Unsupervised Sequence Classification using Sequential Output Statistics
On Fairness and Calibration
The Numerics of GANs
Bandits Dueling on Partially Ordered Sets
Variational Laws of Visual Attention for Dynamic Scenes
Sparse Approximate Conic Hulls
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
Non-convex Finite-Sum Optimization Via SCSG Methods
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation
Attentional Pooling for Action Recognition
Trimmed Density Ratio Estimation
Language Modeling with Recurrent Highway Hypernetworks
Deanonymization in the Bitcoin P2P Network
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization
Scalable Variational Inference for Dynamical Systems
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis
Asynchronous Parallel Coordinate Minimization for MAP Inference
Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding
Query Complexity of Clustering with Side Information
Thinking Fast and Slow with Deep Learning and Tree Search
Fast-Slow Recurrent Neural Networks
Learning to Inpaint for Image Compression
Online Prediction with Selfish Experts
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization
Matching neural paths: transfer from recognition to correspondence search
Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data
Nonbacktracking Bounds on the Influence in Independent Cascade Models
Integration Methods and Optimization Algorithms
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples
Subset Selection and Summarization in Sequential Data
Question Asking as Program Generation
Inductive Representation Learning on Large Graphs
Sharpness, Restart and Acceleration
Matrix Norm Estimation from a Few Entries
Convolutional Phase Retrieval
Predictive-State Decoders: Encoding the Future into Recurrent Networks
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding
Gradient Descent Can Take Exponential Time to Escape Saddle Points
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
Learning Overcomplete HMMs
GP CaKe: Effective brain connectivity with causal kernels
Contrastive Learning for Image Captioning
One-Sided Unsupervised Domain Mapping
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes
First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization
Online multiclass boosting
Self-Normalizing Neural Networks
Unsupervised learning of object frames by dense equivariant image labelling
One-Shot Imitation Learning
Detrended Partial Cross Correlation for Brain Connectivity Analysis
Label Distribution Learning Forests
Learning to Pivot with Adversarial Networks
Differentiable Learning of Submodular Functions
On Blackbox Backpropagation and Jacobian Sensing
Neural Discrete Representation Learning
A Greedy Approach for Budgeted Maximum Inner Product Search
Saliency-based Sequential Image Attention with Multiset Prediction
Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation?
Policy Gradient With Value Function Approximation For Collective Multiagent Planning
Adversarial Symmetric Variational Autoencoder
End-to-End Differentiable Proving
A New Theory for Matrix Completion
Controllable Invariance through Adversarial Feature Learning
Introspective Classification with Convolutional Nets
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Compression-aware Training of Deep Networks
Matching on Balanced Nonlinear Representations for Treatment Effects Estimation
Solving Most Systems of Random Quadratic Equations
Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces
Decoupling "when to update" from "how to update"
Lower bounds on the robustness to adversarial perturbations
A Screening Rule for l1-Regularized Ising Model Estimation
Minimizing a Submodular Function from Samples
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
Safe Model-based Reinforcement Learning with Stability Guarantees
Learning Efficient Object Detection Models with Knowledge Distillation
Improved Dynamic Regret for Non-degenerate Functions
Deep Mean-Shift Priors for Image Restoration
Safe and Nested Subgame Solving for Imperfect-Information Games
Recycling Privileged Learning and Distribution Matching for Fairness
Geometric Descent Method for Convex Composite Minimization
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
Efficient Online Linear Optimization with Approximation Algorithms
Nonparametric Online Regression while Learning the Metric
Unsupervised Image-to-Image Translation Networks
Coded Distributed Computing for Inverse Problems
Convergence Analysis of Two-layer Neural Networks with ReLU Activation
Avoiding Discrimination through Causal Reasoning
Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions
Adversarial Surrogate Losses for Ordinal Regression
Learning multiple visual domains with residual adapters
Multimodal Learning and Reasoning for Visual Question Answering
Diffusion Approximations for Online Principal Component Estimation and Global Convergence
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms
Learning with Average Top-k Loss
Hypothesis Transfer Learning via Transformation Functions
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems
MarrNet: 3D Shape Reconstruction via 2.5D Sketches
Mixture-Rank Matrix Approximation for Collaborative Filtering
Toward Multimodal Image-to-Image Translation
On the Model Shrinkage Effect of Gamma Process Edge Partition Models
Learning Spherical Convolution for Fast Features from 360° Imagery
Preventing Gradient Explosions in Gated Recurrent Units
Towards Accurate Binary Convolutional Neural Network
Gated Recurrent Convolution Neural Network for OCR
Learning a Multi-View Stereo Machine
Variable Importance Using Decision Trees
Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks
Structured Embedding Models for Grouped Data
Universal Style Transfer via Feature Transforms
f-GANs in an Information Geometric Nutshell
Uprooting and Rerooting Higher-Order Graphical Models
On Structured Prediction Theory with Calibrated Convex Surrogate Losses
MaskRNN: Instance Level Video Object Segmentation
Inference in Graphical Models via Semidefinite Programming Hierarchies
Phase Transitions in the Pooled Data Problem
A Linear-Time Kernel Goodness-of-Fit Test
k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms
Learning to See Physics via Visual De-animation
Cortical microcircuits as gated-recurrent neural networks
Parametric Simplex Method for Sparse Learning
A simple model of recognition and recall memory
Interactive Submodular Bandit
From Parity to Preference-based Notions of Fairness in Classification
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings
Pose Guided Person Image Generation
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs
Scalable Generalized Linear Bandits: Online Computation and Hashing
Inferring Generative Model Structure with Static Analysis
Group Sparse Additive Machine
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models
On the Consistency of Quick Shift
Dilated Recurrent Neural Networks
Decoding with Value Networks for Neural Machine Translation
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis
Deep Subspace Clustering Networks
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning
Concentration of Multilinear Functions of the Ising Model with Applications to Network Data
A General Framework for Robust Interactive Learning
Maxing and Ranking with Few Assumptions
Multi-view Matrix Factorization for Linear Dynamical System Estimation
Experimental Design for Learning Causal Graphs with Latent Variables
Unsupervised Learning of Disentangled Representations from Video
On clustering network-valued data
Online Dynamic Programming
Active Learning from Peers
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models
Learning to Model the Tail
When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent
Stochastic Mirror Descent in Variationally Coherent Optimization Problems
Real Time Image Saliency for Black Box Classifiers
Linear Time Computation of Moments in Sum-Product Networks
Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference
Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples
Kernel functions based on triplet comparisons
A Meta-Learning Perspective on Cold-Start Recommendations for Items
An Error Detection and Correction Framework for Connectomics
Convergence of Gradient EM on Multi-component Mixture of Gaussians
Affinity Clustering: Hierarchical Clustering at Scale
Stochastic Submodular Maximization: The Case of Coverage Functions
Neural Expectation Maximization
Predicting Scene Parsing and Motion Dynamics in the Future
Efficient and Flexible Inference for Stochastic Systems
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process
Kernel Feature Selection via Conditional Covariance Minimization
Subspace Clustering via Tangent Cones
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Cross-Spectral Factor Analysis
A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening
Few-Shot Adversarial Domain Adaptation
Practical Hash Functions for Similarity Estimation and Dimensionality Reduction
The Scaling Limit of High-Dimensional Online Independent Component Analysis
Unsupervised Transformation Learning via Convex Relaxations
Approximation Algorithms for $\ell_0$-Low Rank Approximation
On Frank-Wolfe and Equilibrium Computation
Neural Variational Inference and Learning in Undirected Graphical Models
Learning Mixture of Gaussians with Streaming Data
Learning Hierarchical Information Flow with Recurrent Neural Modules
Spectral Mixture Kernels for Multi-Output Gaussian Processes
The power of absolute discounting: all-dimensional distribution estimation
Gradient Episodic Memory for Continual Learning
Protein Interface Prediction using Graph Convolutional Networks
Acceleration and Averaging in Stochastic Descent Dynamics
Random Projection Filter Bank for Time Series Data
Style Transfer from Non-Parallel Text by Cross-Alignment
Towards Generalization and Simplicity in Continuous Control
Modulating early visual processing by language
Z-Forcing: Training Stochastic Recurrent Networks
Inverse Reward Design
Causal Effect Inference with Deep Latent-Variable Models
Effective Parallelisation for Machine Learning
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness
Filtering Variational Objectives
Good Semi-supervised Learning That Requires a Bad GAN
Clustering Stable Instances of Euclidean k-means.
Robust Conditional Probabilities
Learning Linear Dynamical Systems via Spectral Filtering
Communication-Efficient Distributed Learning of Discrete Distributions
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities
Poincaré Embeddings for Learning Hierarchical Representations
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity
Scalable Log Determinants for Gaussian Process Kernel Learning
Learning with Bandit Feedback in Potential Games
Generalizing GANs: A Turing Perspective
Statistical Cost Sharing
Boltzmann Exploration Done Right
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons
Learning Combinatorial Optimization Algorithms over Graphs
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains
Learned in Translation: Contextualized Word Vectors
Invariance and Stability of Deep Convolutional Representations
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
Spectrally-normalized margin bounds for neural networks
Population Matching Discrepancy and Applications in Deep Learning
Hierarchical Clustering Beyond the Worst-Case
The Expressive Power of Neural Networks: A View from the Width
Countering Feedback Delays in Multi-Agent Learning
Asynchronous Coordinate Descent under More Realistic Assumptions
Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls
Optimal Shrinkage of Singular Values Under Random Data Contamination
Efficient Second-Order Online Kernel Learning with Adaptive Embedding
Implicit Regularization in Matrix Factorization
Early stopping for kernel boosting algorithms: A general analysis with localized complexities
Value Prediction Network
A Learning Error Analysis for Structured Prediction with Approximate Inference
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma
Recurrent Ladder Networks
Gaussian Quadrature for Kernel Features
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability
A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control
Estimating Mutual Information for Discrete-Continuous Mixtures
Unbounded cache model for online language modeling with open vocabulary
Fader Networks:Manipulating Images by Sliding Attributes
Action Centered Contextual Bandits
Collaborative Deep Learning in Fixed Topology Networks
Exploring Generalization in Deep Learning
Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos
Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space
Attention is All you Need
Predictive State Recurrent Neural Networks
Learning Neural Representations of Human Cognition across Many fMRI Studies
Self-Supervised Intrinsic Image Decomposition
A KL-LUCB algorithm for Large-Scale Crowdsourcing
Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction
Gradient Methods for Submodular Maximization
Parameter-Free Online Learning via Model Selection
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization
Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra
Permutation-based Causal Inference Algorithms with Interventions
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
Training Quantized Nets: A Deeper Understanding
Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods
Multiscale Quantization for Fast Similarity Search
Learning Populations of Parameters
The Importance of Communities for Learning to Influence
Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations
Online control of the false discovery rate with decaying memory
Do Deep Neural Networks Suffer from Crowding?
Improved Training of Wasserstein GANs
Clustering with Noisy Queries
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes
Gradient descent GAN optimization is locally stable
Imagination-Augmented Agents for Deep Reinforcement Learning
Learning from Complementary Labels
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
Min-Max Propagation
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Discriminative State Space Models
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
Approximate Supermodularity Bounds for Experimental Design
Dualing GANs
Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference
Approximation and Convergence Properties of Generative Adversarial Learning
AdaGAN: Boosting Generative Models
Finite sample analysis of the GTD Policy Evaluation Algorithms in Markov Setting
From Bayesian Sparsity to Gated Recurrent Nets
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Straggler Mitigation in Distributed Optimization Through Data Encoding
Task-based End-to-end Model Learning in Stochastic Optimization
SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks
Plan, Attend, Generate: Planning for Sequence-to-Sequence Models
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification
Improving the Expected Improvement Algorithm
Hybrid Reward Architecture for Reinforcement Learning
Multi-View Decision Processes: The Helper-AI Problem
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
Robust Imitation of Diverse Behaviors
A Sample Complexity Measure with Applications to Learning Optimal Auctions
Local Aggregative Games
On the Complexity of Learning Neural Networks
Riemannian approach to batch normalization
Online Learning with Transductive Regret
Online Learning with a Hint
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
Variational Inference for Gaussian Process Models with Linear Complexity
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
Renyi Differential Privacy Mechanisms for Posterior Sampling
Identifying Outlier Arms in Multi-Armed Bandit
Self-supervised Learning of Motion Capture
EEG-GRAPH: A Factor-Graph-Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms
Identification of Gaussian Process State Space Models
Learning Graph Representations with Embedding Propagation
K-Medoids For K-Means Seeding
Real-Time Bidding with Side Information
Fair Clustering Through Fairlets
Bayesian Optimization with Gradients
Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models
Stochastic and Adversarial Online Learning without Hyperparameters
Active Exploration for Learning Symbolic Representations
A-NICE-MC: Adversarial Training for MCMC
Teaching Machines to Describe Images with Natural Language Feedback
Triangle Generative Adversarial Networks
PRUNE: Preserving Proximity and Global Ranking for Network Embedding
Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Selective Classification for Deep Neural Networks
Perturbative Black Box Variational Inference
Online Reinforcement Learning in Stochastic Games
Fast, Sample-Efficient Algorithms for Structured Phase Retrieval
Group Additive Structure Identification for Kernel Nonparametric Regression
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Position-based Multiple-play Bandit Problem with Unknown Position Bias
Polynomial time algorithms for dual volume sampling
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models
Maximum Margin Interval Trees
A simple neural network module for relational reasoning
Online Learning for Multivariate Hawkes Processes
Minimax Estimation of Bandable Precision Matrices
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes
Hash Embeddings for Efficient Word Representations
Monte-Carlo Tree Search by Best Arm Identification
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
Hindsight Experience Replay
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice
Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex
Working hard to know your neighbor's margins: Local descriptor learning loss
Context Selection for Embedding Models
A Unified Approach to Interpreting Model Predictions
DropoutNet: Addressing Cold Start in Recommender Systems
Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization
Multi-Task Learning for Contextual Bandits
Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation
Non-Stationary Spectral Kernels
SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud
Adaptive Classification for Prediction Under a Budget
Robust Optimization for Non-Convex Objectives
Overcoming Catastrophic Forgetting by Incremental Moment Matching
Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds
Stochastic Approximation for Canonical Correlation Analysis
Convergence rates of a partition based Bayesian multivariate density estimation method
Affine-Invariant Online Optimization and the Low-rank Experts Problem
Ranking Data with Continuous Labels through Oriented Recursive Partitions
Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs
Targeting EEG/LFP Synchrony with Neural Nets
Simple strategies for recovering inner products from coarsely quantized random projections
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach
Doubly Stochastic Variational Inference for Deep Gaussian Processes
Scalable Model Selection for Belief Networks
Balancing information exposure in social networks
Visual Interaction Networks: Learning a Physics Simulator from Video
Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems
QMDP-Net: Deep Learning for Planning under Partial Observability
Discovering Potential Correlations via Hypercontractivity
Federated Multi-Task Learning
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
Distral: Robust multitask reinforcement learning
Improved Graph Laplacian via Geometric Self-Consistency
Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities
A Minimax Optimal Algorithm for Crowdsourcing
Dual Path Networks
Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach
Deep Reinforcement Learning from Human Preferences
VAE Learning via Stein Variational Gradient Descent
A Decomposition of Forecast Error in Prediction Markets
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks
Safe Adaptive Importance Sampling
Learning Active Learning from Data
Multi-Information Source Optimization
Inverse Filtering for Hidden Markov Models
Non-parametric Structured Output Networks
The Marginal Value of Adaptive Gradient Methods in Machine Learning
Mapping distinct timescales of functional interactions among brain networks
Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication
Learning Low-Dimensional Metrics
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks
Deconvolutional Paragraph Representation Learning
Random Permutation Online Isotonic Regression
Multi-Armed Bandits with Metric Movement Costs
Reconstructing perceived faces from brain activations with deep adversarial neural decoding
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification
Learning A Structured Optimal Bipartite Graph for Co-Clustering
Counterfactual Fairness
Independence clustering (without a matrix)
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems
Triple Generative Adversarial Nets
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly
Prototypical Networks for Few-shot Learning
Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation
Optimized Pre-Processing for Discrimination Prevention
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
Adaptive Active Hypothesis Testing under Limited Information
Scalable Levy Process Priors for Spectral Kernel Learning
On-the-fly Operation Batching in Dynamic Computation Graphs
Dynamic Routing Between Capsules
On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm
Structured Generative Adversarial Networks
Conservative Contextual Linear Bandits
FALKON: An Optimal Large Scale Kernel Method
Successor Features for Transfer in Reinforcement Learning
Nonlinear Acceleration of Stochastic Algorithms
Variational Memory Addressing in Generative Models
Deep Hyperspherical Learning
Conic Scan-and-Cover algorithms for nonparametric topic modeling
Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction
Recursive Sampling for the Nystrom Method
Joint distribution optimal transportation for domain adaptation
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations
YASS: Yet Another Spike Sorter
Incorporating Side Information by Adaptive Convolution
Universal consistency and minimax rates for online Mondrian Forests
Multiresolution Kernel Approximation for Gaussian Process Regression
Influence Maximization with $\varepsilon$-Almost Submodular Threshold Functions
Diving into the shallows: a computational perspective on large-scale shallow learning
Collapsed variational Bayes for Markov jump processes
Welfare Guarantees from Data
Visual Reference Resolution using Attention Memory for Visual Dialog
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Reducing Reparameterization Gradient Variance
On the Optimization Landscape of Tensor Decompositions
High-Order Attention Models for Visual Question Answering
A multi-agent reinforcement learning model of common-pool resource appropriation
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
Sparse convolutional coding for neuronal assembly detection
Concrete Dropout
Subset Selection under Noise
Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization
Bayesian GAN
Off-policy evaluation for slate recommendation
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Collecting Telemetry Data Privately
Adaptive Batch Size for Safe Policy Gradients
Certified Defenses for Data Poisoning Attacks
Gaussian process based nonlinear latent structure discovery in multivariate spike train data
Near Optimal Sketching of Low-Rank Tensor Regression
Eigen-Distortions of Hierarchical Representations
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Quantifying how much sensory information in a neural code is relevant for behavior
Dynamic-Depth Context Tree Weighting
Multi-Objective Non-parametric Sequential Prediction
Process-constrained batch Bayesian optimisation
Sparse Embedded $k$-Means Clustering
Deep Sets
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
On Optimal Generalizability in Parametric Learning
Spherical convolutions and their application in molecular modelling
Neural system identification for large populations separating “what” and “where”
Multi-output Polynomial Networks and Factorization Machines
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding
Bayesian Compression for Deep Learning
Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit
Streaming Sparse Gaussian Process Approximations
A Regularized Framework for Sparse and Structured Neural Attention
A Universal Analysis of Large-Scale Regularized Least Squares Solutions
Tractability in Structured Probability Spaces
Is the Bellman residual a bad proxy?
Dynamic Importance Sampling for Anytime Bounds of the Partition Function
Clustering Billions of Reads for DNA Data Storage
Ensemble Sampling
Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee
Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem
Learning to Compose Domain-Specific Transformations for Data Augmentation
Graph Matching via Multiplicative Update Algorithm
Stein Variational Gradient Descent as Gradient Flow
Differentially private Bayesian learning on distributed data
Hierarchical Attentive Recurrent Tracking
Shallow Updates for Deep Reinforcement Learning
Generalization Properties of Learning with Random Features
Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search
Tomography of the London Underground: a Scalable Model for Origin-Destination Data
Regret Minimization in MDPs with Options without Prior Knowledge
Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition
Variance-based Regularization with Convex Objectives
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback
Unbiased estimates for linear regression via volume sampling
Wasserstein Learning of Deep Generative Point Process Models
Learning Causal Structures Using Regression Invariance
Partial Hard Thresholding: Towards A Principled Analysis of Support Recovery
Reinforcement Learning under Model Mismatch
Deep Voice 2: Multi-Speaker Neural Text-to-Speech
Rotting Bandits
Adversarial Ranking for Language Generation
Model-Powered Conditional Independence Test
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent
Continual Learning with Deep Generative Replay
Deep Lattice Networks and Partial Monotonic Functions
Decomposable Submodular Function Minimization: Discrete and Continuous
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
Mean Field Residual Networks: On the Edge of Chaos
Beyond Parity: Fairness Objectives for Collaborative Filtering
Cold-Start Reinforcement Learning with Softmax Policy Gradient
Gauging Variational Inference
Deep Recurrent Neural Network-Based Identification of Precursor microRNAs
Alternating Estimation for Structured High-Dimensional Multi-Response Models
A Bayesian Data Augmentation Approach for Learning Deep Models
State Aware Imitation Learning
Estimation of the covariance structure of heavy-tailed distributions
Robust Estimation of Neural Signals in Calcium Imaging
Premise Selection for Theorem Proving by Deep Graph Embedding
Convolutional Gaussian Processes
Principles of Riemannian Geometry in Neural Networks
Bridging the Gap Between Value and Policy Based Reinforcement Learning
VAIN: Attentional Multi-agent Predictive Modeling
An Empirical Bayes Approach to Optimizing Machine Learning Algorithms
Information-theoretic analysis of generalization capability of learning algorithms
Multitask Spectral Learning of Weighted Automata
Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
Multi-way Interacting Regression via Factorization Machines
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
An Empirical Study on The Properties of Random Bases for Kernel Methods
Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search
Dual Discriminator Generative Adversarial Nets
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
How regularization affects the critical points in linear networks
SGD Learns the Conjugate Kernel Class of the Network
Differentiable Learning of Logical Rules for Knowledge Base Reasoning
Scalable Demand-Aware Recommendation
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting
Practical Data-Dependent Metric Compression with Provable Guarantees
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM
Variational Inference via $\chi$ Upper Bound Minimization
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks
Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning
Practical Locally Private Heavy Hitters
Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences
Fisher GAN
Masked Autoregressive Flow for Density Estimation
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe
Parallel Streaming Wasserstein Barycenters
Dynamic Revenue Sharing
Fitting Low-Rank Tensors in Constant Time
Runtime Neural Pruning
An inner-loop free solution to inverse problems using deep neural networks
Nonlinear random matrix theory for deep learning
Generative Local Metric Learning for Kernel Regression
Elementary Symmetric Polynomials for Optimal Experimental Design
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation
Pixels to Graphs by Associative Embedding
Expectation Propagation for t-Exponential Family Using q-Algebra
Noise-Tolerant Interactive Learning Using Pairwise Comparisons
Inhomogeneous Hypergraph Clustering with Applications
Natural Value Approximators: Learning when to Trust Past Estimates
Few-Shot Learning Through an Information Retrieval Lens
Collaborative PAC Learning
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization
The Reversible Residual Network: Backpropagation Without Storing Activations
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
Deep Supervised Discrete Hashing
OnACID: Online Analysis of Calcium Imaging Data in Real Time
Zap Q-Learning
MMD GAN: Towards Deeper Understanding of Moment Matching Network
Training Deep Networks without Learning Rates Through Coin Betting
Consistent Robust Regression
A graph-theoretic approach to multitasking
Compatible Reward Inverse Reinforcement Learning
Bayesian Dyadic Trees and Histograms for Regression
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs
Learning ReLUs via Gradient Descent
Neural Program Meta-Induction
Hiding Images in Plain Sight: Deep Steganography
Generating steganographic images via adversarial training
Stabilizing Training of Generative Adversarial Networks through Regularization
Alternating minimization for dictionary learning with random initialization
PixelGAN Autoencoders
Consistent Multitask Learning with Nonlinear Output Relations
Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models
Hierarchical Methods of Moments
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems
Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization
Revenue Optimization with Approximate Bid Predictions
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts
Adaptive Clustering through Semidefinite Programming
Lookahead Bayesian Optimization with Inequality Constraints
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks
Learning Multiple Tasks with Multilinear Relationship Networks
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
Deep Dynamic Poisson Factorization Model
Learning Chordal Markov Networks via Branch and Bound
Repeated Inverse Reinforcement Learning
Deep Hyperalignment
Predicting User Activity Level In Point Processes With Mass Transport Equation
Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions
Reliable Decision Support using Counterfactual Models
Online to Offline Conversions, Universality and Adaptive Minibatch Sizes
A New Alternating Direction Method for Linear Programming
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure
Deliberation Networks: Sequence Generation Beyond One-Pass Decoding
Model evidence from nonequilibrium simulations
Deep Learning with Topological Signatures
From which world is your graph
Regret Analysis for Continuous Dueling Bandit
Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues
Positive-Unlabeled Learning with Non-Negative Risk Estimator
Optimal Sample Complexity of M-wise Data for Top-K Ranking
Learned D-AMP: Principled Neural Network based Compressive Image Recovery
NeuralFDR: Learning Discovery Thresholds from Hypothesis Features
Minimal Exploration in Structured Stochastic Bandits
Regularized Modal Regression with Applications in Cognitive Impairment Prediction
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
Best Response Regression
Linear regression without correspondence
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
Learning Affinity via Spatial Propagation Networks
Cost efficient gradient boosting
Online Convex Optimization with Stochastic Constraints
Testing and Learning on Distributions with Symmetric Noise Invariance
Max-Margin Invariant Features from Transformed Unlabelled Data
Translation Synchronization via Truncated Least Squares
Adaptive stimulus selection for optimizing neural population responses
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering
DPSCREEN: Dynamic Personalized Screening
Generalized Linear Model Regression under Distance-to-set Penalties
Tensor Biclustering
Adaptive Bayesian Sampling with Monte Carlo EM
Shape and Material from Sound
Accelerated consensus via Min-Sum Splitting
Flexible statistical inference for mechanistic models of neural dynamics
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data
Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
Linearly constrained Gaussian processes
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