paper.list

Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video
Zero-shot Learning via Simultaneous Generating and Learning
Ask not what AI can do for you, but what AI should do: Towards a framework of task delegability
Stand-Alone Self-Attention in Vision Models
High Fidelity Video Prediction with Large Neural Nets
Unsupervised learning of object structure and dynamics from videos
TensorPipe: Easy Scaling with Micro-Batch Pipeline Parallelism
Meta-Learning with Implicit Gradients
Adversarial Examples Are Not Bugs, They Are Features
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks
FreeAnchor: Learning to Match Anchors for Visual Object Detection
Differentially Private Hypothesis Selection
New Differentially Private Algorithms for Learning Mixtures of Well-Separated Gaussians
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation
Multi-Resolution Weak Supervision for Sequential Data
DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision
The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
Generalized Sliced Wasserstein Distances
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
Blind Super-Resolution Kernel Estimation using an Internal-GAN
Noise-tolerant fair classification
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection
Joint-task Self-supervised Learning for Temporal Correspondence
Provable Gradient Variance Guarantees for Black-Box Variational Inference
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation
Experience Replay for Continual Learning
Deep ReLU Networks Have Surprisingly Few Activation Patterns
Chasing Ghosts: Instruction Following as Bayesian State Tracking
Block Coordinate Regularization by Denoising
Reducing Noise in GAN Training with Variance Reduced Extragradient
Learning Erdos-Renyi Random Graphs via Edge Detecting Queries
A Primal-Dual link between GANs and Autoencoders
muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation
Invert to Learn to Invert
Equitable Stable Matchings in Quadratic Time
Zero-Shot Semantic Segmentation
Metric Learning for Adversarial Robustness
DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
Batched Multi-armed Bandits Problem
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
Differentially Private Bayesian Linear Regression
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling
CPM-Nets: Cross Partial Multi-View Networks
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling
SySCD: A System-Aware Parallel Coordinate Descent Algorithm
Importance Weighted Hierarchical Variational Inference
RSN: Randomized Subspace Newton
Trust Region-Guided Proximal Policy Optimization
Adversarial Self-Defense for Cycle-Consistent GANs
Towards closing the gap between the theory and practice of SVRG
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
ETNet: Error Transition Network for Arbitrary Style Transfer
No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms
Deep Equilibrium Models
Saccader: Accurate, Interpretable Image Classification with Hard Attention
Multiway clustering via tensor block models
Regret Minimization for Reinforcement Learning on Multi-Objective Online Markov Decision Processes
NAT: Neural Architecture Transformer for Accurate and Compact Architectures
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression
Network Pruning via Transformable Architecture Search
Differentiable Cloth Simulation for Inverse Problems
Poisson-randomized Gamma Dynamical Systems
Volumetric Correspondence Networks for Optical Flow
Learning Conditional Deformable Templates with Convolutional Networks
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data
Efficient Symmetric Norm Regression via Linear Sketching
RUBi: Reducing Unimodal Biases in Visual Question Answering
Reducing Scene Bias of Convolutional Neural Networks for Human Action Understanding
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution
DATA: Differentiable ArchiTecture Approximation
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge
Memory-oriented Decoder for Light Field Salient Object Detection
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition
Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels
Powerset Convolutional Neural Networks
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums
Efficient 3D Deep Learning via Point-Based Representation and Voxel-Based Convolution
Deep Learning without Weight Transport
Combinatorial Bandits with Relative Feedback
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme
Joint Optimizing of Cycle-Consistent Networks
Explicit Disentanglement of Appearance and Perspective in Generative Models
Polynomial Cost of Adaptation for X-Armed Bandits
Learning to Propagate for Graph Meta-Learning
Secretary Ranking with Minimal Inversions
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes
Learning Perceptual Inference by Contrasting
Selecting the independent coordinates of manifolds with large aspect ratios
Region-specific Diffeomorphic Metric Mapping
Subset Selection via Supervised Facility Location
Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations
Reconciling λ-Returns with Experience Replay
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs
A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation
Combinatorial Inference against Label Noise
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning
Convolution with even-sized kernels and symmetric padding
On The Classification-Distortion-Perception Tradeoff
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up
Online sampling from log-concave distributions
Envy-Free Classification
Finding Friend and Foe in Multi-Agent Games
Computer Vision with a Single (Robust) Classifier
Gated CRF Loss for Weakly Supervised Semantic Image Segmentation
Model Compression with Adversarial Robustness: A Unified Optimization Framework
Neuron Communication Networks
CondConv: Conditionally Parameterized Convolutions for Efficient Inference
Regression Planning Networks
Twin Auxilary Classifiers GAN
Conditional Structure Generation through Graph Variational Generative Adversarial Nets
Distributional Policy Optimization: An Alternative Approach for Continuous Control
Sampling Sketches for Concave Sublinear Functions of Frequencies
Deliberative Explanations: visualizing network insecurities
Computing Full Conformal Prediction Set with Approximate Homotopy
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards
Multi-View Reinforcement Learning
Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution
Neural Diffusion Distance for Image Segmentation
Fine-grained Optimization of Deep Neural Networks
Extending Stein’s Unbiased Risk Estimator To Train Deep Denoisers with Correlated Pairs of Noisy Images
Wibergian Learning of Continuous Energy Functions
Hyperspherical Prototype Networks
Expressive power of tensor-network factorizations for probabilistic modelling
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs
SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points
Efficient Meta Learning via Minibatch Proximal Update
Unconstrained Monotonic Neural Networks
Guided Similarity Separation for Image Retrieval
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
Strategizing against No-regret Learners
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
Hierarchical Optimal Transport for Document Representation
Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes
Positional Normalization
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Quadratic Video Interpolation
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies
Incremental Scene Synthesis
Self-Supervised Generalisation with Meta Auxiliary Learning
Variational Denoising Network: Toward Blind Noise Modeling and Removal
Fast Sparse Group Lasso
Learnable Tree Filter for Structure-preserving Feature Transform
Data-Dependence of Plateau Phenomenon in Learning with Neural Network --- Statistical Mechanical Analysis
Coordinated hippocampal-entorhinal replay as structural inference
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
On the Curved Geometry of Accelerated Optimization
Multi-marginal Wasserstein GAN
Better Exploration with Optimistic Actor Critic
Importance Resampling for Off-policy Prediction
The Label Complexity of Active Learning from Observational Data
Meta-Learning Representations for Continual Learning
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training
Visualizing the PHATE of Neural Networks
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers
Nonconvex Low-Rank Tensor Completion from Noisy Data
Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization
Channel Gating Neural Networks
Neural networks grown and self-organized by noise
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
Variational Structured Semantic Inference for Diverse Image Captioning
Mapping State Space using Landmarks for Universal Goal Reaching
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks
Random deep neural networks are biased towards simple functions
XNAS: Neural Architecture Search with Expert Advice
CNN^{2}: Viewpoint Generalization via a Binocular Vision
Generalized Off-Policy Actor-Critic
DAC: The Double Actor-Critic Architecture for Learning Options
Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models
Controlling Neural Level Sets
Blended Matching Pursuit
An Improved Analysis of Training Over-parameterized Deep Neural Networks
Controllable Text to Image Generation
Improving Textual Network Learning with Variational Homophilic Embeddings
Rethinking Generative Coverage: A Pointwise Guaranteed Approach
The Randomized Midpoint Method for Log-Concave Sampling
Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update
Fully Neural Network based Model for General Temporal Point Processes
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks
Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design
Provably Powerful Graph Networks
Order Optimal One-Shot Distributed Learning
Information Competing Process for Learning Diversified Representations
GENO -- GENeric Optimization for Classical Machine Learning
Conditional Independence Testing using Generative Adversarial Networks
Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function
Partitioning Structure Learning for Segmented Linear Regression Trees
A Tensorized Transformer for Language Modeling
Kernel Stein Tests for Multiple Model Comparison
Disentangled behavioural representations
More Is Less: Learning Efficient Video Representations by Temporal Aggregation Module
Rethinking the CSC Model for Natural Images
Integrating Generative and Discriminative Sparse Kernel Machines for Multi-class Active Learning
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
Perceiving the arrow of time in autoregressive motion
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
Hyper-Graph-Network Decoders for Block Codes
Large Scale Markov Decision Processes with Changing Rewards
Multiview Aggregation for Learning Category-Specific Shape Reconstruction
Semi-Parametric Dynamic Contextual Pricing
Nearly Linear-Time, Deterministic Algorithm for Maximizing (Non-Monotone) Submodular Functions Under Cardinality Constraint
Initialization of ReLUs for Dynamical Isometry
Gradient Information for Representation and Modeling
SpiderBoost and Momentum: Faster Variance Reduction Algorithms
Minimax rates of estimating approximate differential privacy
Backprop with Approximate Activations for Memory-efficient Network Training
Training Image Estimators without Image Ground Truth
Deep Structured Prediction for Facial Landmark Detection
Information-Theoretic Confidence Bounds for Reinforcement Learning
Transfer Anomaly Detection by Inferring Latent Domain Representations
Total Least Squares Regression in Input Sparsity Time
Park: An Open Platform for Learning-Augmented Computer Systems
Adapting Neural Networks for the Estimation of Treatment Effects
Learning Transferable Graph Exploration
Conformal Prediction Under Covariate Shift
Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation
Asymmetric Valleys: Beyond Sharp and Flat Local Minima
Positive-Unlabeled Compression on the Cloud
Direct Estimation of Differential Functional Graphical Model
On the Calibration of Multiclass Classification with Rejection
Third-Person Visual Imitation Learning via Decoupled Hierarchical Control
Stagewise Training Accelerates Convergence of Testing Error Over SGD
Learning Robust Options by Conditional Value at Risk Optimization
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems
On Learning Over-parameterized Neural Networks: A Functional Approximation Prospective
Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries
Visual Sequence Learning in Hierarchical Prediction Networks and Primate Visual Cortex
Dual Variational Generation for Low Shot Heterogeneous Face Recognition
Discovering Neural Wirings
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems
Knowledge Extraction with No Observable Data
PAC-Bayes under potentially heavy tails
One-Shot Object Detection with Co-Attention and Co-Excitation
Quaternion Knowledge Graph Embeddings
Glyce: Glyph-vectors for Chinese Character Representations
Turbo Autoencoder: Deep learning based channel code for point-to-point communication channels
Heterogeneous Graph Learning for Visual Commonsense Reasoning
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components
Identifying Causal Effects via Context-specific Independence Relations
Bridging Machine Learning and Logical Reasoning by Abductive Learning
Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods
A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization
Regularizing Trajectory Optimization with Denoising Autoencoders
Learning Hierarchical Priors in VAEs
Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits
Safe Exploration for Interactive Machine Learning
Addressing Failure Detection by Learning Model Confidence
Combinatorial Bayesian Optimization using the Graph Cartesian Product
Fooling Neural Network Interpretations via Adversarial Model Manipulation
On Lazy Training in Differentiable Programming
Quality Aware Generative Adversarial Networks
Copula-like Variational Inference
Implicit Regularization for Optimal Sparse Recovery
Locally Private Gaussian Estimation
Multi-mapping Image-to-Image Translation via Learning Disentanglement
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
Structured Decoding for Non-Autoregressive Machine Translation
Learning Temporal Pose Estimation from Sparsely-Labeled Videos
Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition
Real-Time Reinforcement Learning
Robust Multi-agent Counterfactual Prediction
Approximate Inference Turns Deep Networks into Gaussian Processes
Deep Signatures
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits
Convergent Policy Optimization for Safe Reinforcement Learning
Augmented Neural ODEs
Thompson Sampling for Multinomial Logit Contextual Bandits
Backpropagation-Friendly Eigendecomposition
FastSpeech: Fast, Robust and Controllable Text to Speech
Ultrametric Fitting by Gradient Descent
Distinguishing Distributions When Samples Are Strategically Transformed
Implicit Regularization of Discrete Gradient Dynamics in Deep Linear Neural Networks
Deep Set Prediction Networks
DppNet: Approximating Determinantal Point Processes with Deep Networks
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
Neural Lyapunov Control
Fully Dynamic Consistent Facility Location
A Stickier Benchmark for General-Purpose Language Understanding Systems
A Flexible Generative Framework for Graph-based Semi-supervised Learning
Self-normalization in Stochastic Neural Networks
Optimal Decision Tree with Noisy Outcomes
Meta-Curvature
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning
KerGM: Kernelized Graph Matching
Transfusion: Understanding Transfer Learning for Medical Imaging
Adversarial training for free!
Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients
Implicitly learning to reason in first-order logic
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration
Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric
HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models
First order expansion of convex regularized estimators
Capacity Bounded Differential Privacy
Universal Boosting Variational Inference
SGD on Neural Networks Learns Functions of Increasing Complexity
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk
Making AI Forget You: Data Deletion in Machine Learning
Practical Differentially Private Top-k Selection with Pay-what-you-get Composition
Conformalized Quantile Regression
Thompson Sampling with Information Relaxation Penalties
Deep Generalized Method of Moments for Instrumental Variable Analysis
Learning Sample-Specific Models with Low-Rank Personalized Regression
Dance to Music
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask
Implicit Generation and Modeling with Energy Based Models
Who Learns? Decomposing Learning into Per-Parameter Loss Contribution
Predicting the Politics of an Image Using Webly Supervised Data
Adaptive GNN for Image Analysis and Editing
Ultra Fast Medoid Identification via Correlated Sequential Halving
Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD
Asymptotics for Sketching in Least Squares Regression
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
Exact inference in structured prediction
Coda: An End-to-End Neural Program Decompiler
Bat-G net: Bat-inspired High-Resolution 3D Image Reconstruction using Ultrasonic Echoes
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy
Learning Representations for Time Series Clustering
Variance Reduced Uncertainty Calibration
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits
Unsupervised Keypoint Learning for Guiding Class-conditional Video Prediction
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling
Cross-sectional Learning of Extremal Dependence among Financial Assets
Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG
Compression with Flows via Local Bits-Back Coding
Exact Rate-Distortion in Autoencoders via Echo Noise
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI
Self-Supervised Active Triangulation for 3D Human Pose Reconstruction
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization
Improved Precision and Recall Metric for Assessing Generative Models
A First-order Algorithmic Framework for Distributionally Robust Logistic Regression
PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph
Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise
Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
Learning Generalizable Device Placement Algorithms for Distributed Machine Learning
Uncoupled Regression from Pairwise Comparison Data
Cross Attention Network for Few-shot Classification
A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs
Teaching Multiple Concepts to a Forgetful Learner
Regularized Weighted Low Rank Approximation
Practical and Consistent Estimation of f-Divergences
Approximation Ratios of Graph Neural Networks for Combinatorial Problems
Thinning for Accelerating the Learning of Point Processes
A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models
Differentially Private Markov Chain Monte Carlo
Full-Gradient Representation for Neural Network Visualization
q-means: A quantum algorithm for unsupervised machine learning
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
Limitations of the empirical Fisher approximation
Flow-based Image-to-Image Translation with Feature Disentanglement
Learning dynamic semi-algebraic proofs
Shape and Time Distorsion Loss for Training Deep Time Series Forecasting Models
Understanding attention in graph neural networks
Data Cleansing for Models Trained with SGD
Curvilinear Distance Metric Learning
Semantically-Regularized Logic Graph Embeddings
Modeling Uncertainty by Learning A Hierarchy of Deep Neural Connections
Efficient Graph Generation with Graph Recurrent Attention Networks
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms
Learning Deep Bilinear Transformation for Fine-grained Image Representation
Practical Deep Learning with Bayesian Principles
Training Language GANs from Scratch
Pseudo-Extended Markov chain Monte Carlo
Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate
Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters
On Adversarial Mixup Resynthesis
A Geometric Perspective on Optimal Representations for Reinforcement Learning
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks
Understanding and Improving Layer Normalization
Uncertainty-based Continual Learning with Adaptive Regularization
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
Massively scalable Sinkhorn distances via the Nyström method
Double Quantization for Communication-Efficient Distributed Optimization
Globally optimal score-based learning of directed acyclic graphs in high-dimensions
Multi-relational Poincaré Graph Embeddings
No-Press Diplomacy: Modeling Multi-Agent Gameplay
State Aggregation Learning from Markov Transition Data
Disentangling Influence: Using disentangled representations to audit model predictions
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
Partially Encrypted Deep Learning using Functional Encryption
Decentralized Cooperative Stochastic Bandits
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
Efficient Deep Approximation of GMMs
Learning low-dimensional state embeddings and metastable clusters from time series data
Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes
Kernel Instrumental Variable Regression
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments
Fast Efficient Hyperparameter Tuning for Policy Gradient Methods
Offline Contextual Bayesian Optimization
Making the Cut: A Bandit-based Approach to Tiered Interviewing
Unsupervised Scalable Representation Learning for Multivariate Time Series
A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI
End to end learning and optimization on graphs
Game Design for Eliciting Distinguishable Behavior
When does label smoothing help?
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation
Distribution-Independent PAC Learning of Halfspaces with Massart Noise
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies
Online Learning for Auxiliary Task Weighting for Reinforcement Learning
Blocking Bandits
Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities
Prior-Free Dynamic Auctions with Low Regret Buyers
On Single Source Robustness in Deep Fusion Models
Policy Evaluation with Latent Confounders via Optimal Balance
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
Adaptive Cross-Modal Few-shot Learning
Spectral Modification of Graphs for Improved Spectral Clustering
Hyperbolic Graph Convolutional Neural Networks
Cost Effective Active Search
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks
A Stratified Approach to Robustness for Randomly Smoothed Classifiers
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces
Fair Algorithms for Clustering
Learning Mean-Field Games
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers
Deep imitation learning for molecular inverse problems
Visual Concept-Metaconcept Learning
Adaptive Video-to-Video Synthesis via Network Weight Generation
Neural Similarity Learning
Ordered Memory
MixMatch: A Holistic Approach to Semi-Supervised Learning
Deep Multivariate Quantiles for Novelty Detection
Fast Parallel Algorithms for Statistical Subset Selection Problems
PHYRE: A New Benchmark for Physical Reasoning
How many variables should be entered in a principal component regression equation?
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery
Mutually Regressive Point Processes
Data-driven Estimation of Sinusoid Frequencies
E2-Train: Energy-Efficient Deep Network Training with Data-, Model-, and Algorithm-Level Saving
ANODEV2: A Coupled Neural ODE Framework
Estimating Entropy of Distributions in Constant Space
On the Utility of Learning about Humans for Human-AI Coordination
Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium
Learning in Generalized Linear Contextual Bandits with Stochastic Delays
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
Optimistic Regret Minimization for Extensive-Form Games via Dilated Distance-Generating Functions
On Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
On the Accuracy of Influence Functions for Measuring Group Effects
Face Reconstruction from Voice using Generative Adversarial Networks
Incremental Few-Shot Learning with Attention Attractor Networks
On Testing for Biases in Peer Review
Learning Disentangled Representation for Robust Person Re-identification
Balancing Efficiency and Fairness in On-Demand Ridesourcing
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion
Input Similarity from the Neural Network Perspective
Adaptive Sequence Submodularity
Weight Agnostic Neural Networks
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
Reducing the variance in online optimization by transporting past gradients
Characterizing Bias in Classifiers using Generative Models
Optimal Stochastic and Online Learning with Individual Iterates
Policy Learning for Fairness in Ranking
Off-Policy Evaluation of Generalization for Deep Q-Learning in Binary Reward Tasks
Regularized Gradient Boosting
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Markov Random Fields for Collaborative Filtering
A Step Toward Quantifying Independently Reproducible Machine Learning Research
Scalable Global Optimization via Local Bayesian Optimization
Time-series Generative Adversarial Networks
On Accelerating Training of Transformer-Based Language Models
A Refined Margin Distribution Analysis for Forest Representation Learning
Robustness to Adversarial Perturbations in Learning from Incomplete Data
Exploring Unexplored Tensor Decompositions for Convolutional Neural Networks
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
Adaptive Influence Maximization with Myopic Feedback
Focused Quantization for Sparse CNNs
Quantum Embedding of Knowledge for Reasoning
Optimal Best Markovian Arm Identification with Fixed Confidence
Limiting Extrapolation in Linear Approximate Value Iteration
Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model
Invertible Convolutional Flow
A Latent Variational Framework for Stochastic Optimization
Topology-Preserving Deep Image Segmentation
Connective Cognition Network for Directional Visual Commonsense Reasoning
Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning
Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently
Learning Disentangled Representations for Recommendation
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
In-Place Near Zero-Cost Memory Protection for DNN
Acceleration via Symplectic Discretization of High-Resolution Differential Equations
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex
Mixtape: Breaking the Softmax Bottleneck Efficiently
Variance Reduced Policy Evaluation with Smooth Function Approximation
Learning GANs and Ensembles Using Discrepancy
Co-Generation with GANs using AIS based HMC
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs
Abstract Reasoning with Distracting Features
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer
Adversarial Training and Robustness for Multiple Perturbations
Doubly-Robust Lasso Bandit
DM2C: Deep Mixed-Modal Clustering
MaCow: Masked Convolutional Generative Flow
Learning by Abstraction: The Neural State Machine for Visual Reasoning
Adaptive Gradient-Based Meta-Learning Methods
Equipping Experts/Bandits with Long-term Memory
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning
Scalable inference of topic evolution via models for latent geometric structures
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network
Deep Active Learning with a Neural Architecture Search
Efficiently escaping saddle points on manifolds
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
DFNets: Spectral CNNs for Graphs with Feedback-looped Filters
Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning
Comparing Unsupervised Word Translation Methods Step by Step
Learning from Crap Data via Generation
Constrained deep neural network architecture search for IoT devices accounting hardware calibration
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
Iterative Least Trimmed Squares for Mixed Linear Regression
Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces
Divergence-Augmented Policy Optimization
Intrinsic dimension of data representations in deep neural networks
Towards a Zero-One Law for Column Subset Selection
Compositional De-Attention Networks
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
Mining GOLD Samples for Conditional GANs
Deep Model Transferability from Attribution Maps
Fully Parameterized Quantile Function for Distributional Reinforcement Learning
Direct Optimization through 
arg
max
 for Discrete Variational Auto-Encoder
Distributional Reward Decomposition for Reinforcement Learning
L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise
Convergence Guarantees for Adaptive Bayesian Quadrature Methods
Progressive Augmentation of GANs
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization
Meta-Surrogate Benchmarking for Hyperparameter Optimization
Learning to Perform Local Rewriting for Combinatorial Optimization
Anti-efficient encoding in emergent communication
Singleshot : a scalable Tucker tensor decomposition
Neural Machine Translation with Soft Prototype
Reliable training and estimation of variance networks
On the Statistical Properties of Multilabel Learning
Bayesian Learning of Sum-Product Networks
Bayesian Batch Active Learning as Sparse Subset Approximation
Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
Variational Bayesian Decision-making for Continuous Utilities
The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks
Single-Model Uncertainties for Deep Learning
Is Deeper Better only when Shallow is Good?
Wasserstein Weisfeiler-Lehman Graph Kernels
Domain Generalization via Model-Agnostic Learning of Semantic Features
Grid Saliency for Context Explanations of Semantic Segmentation
First-order methods almost always avoid saddle points: The case of Vanishing step-sizes
Maximum Mean Discrepancy Gradient Flow
Oblivious Sampling Algorithms for Private Data Analysis
Semi-supervisedly Co-embedding Attributed Networks
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders
Nonstochastic Multiarmed Bandits with Unrestricted Delays
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
Code Generation as Dual Task of Code Summarization
Diffeomorphic Temporal Alignment Networks
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior
On the Power and Limitations of Random Features for Understanding Neural Networks
Efficient Pure Exploration in Adaptive Round model
Multi-objects Generation with Amortized Structural Regularization
Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time
DetNAS: Backbone Search for Object Detection
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates
Fast AutoAugment
On the Convergence Rate of Training Recurrent Neural Networks in the Overparameterized Regime
Interval timing in deep reinforcement learning agents
Graph-based Discriminators: Sample Complexity and Expressiveness
Large Scale Structure of Neural Network Loss Landscapes
Learning Nonsymmetric Determinantal Point Processes
Hypothesis Set Stability and Generalization
Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
Precision-Recall Balanced Topic Modelling
Learning Sparse Distributions using Iterative Hard Thresholding
Discriminative Topic Modeling with Logistic LDA
Quantum Wasserstein Generative Adversarial Networks
Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion
Hyperparameter Learning via Distributional Transfer
Discriminator optimal transport
High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes
Are Anchor Points Really Indispensable in Label-Noise Learning?
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations
Differentiable Sorting using Optimal Transport: The Sinkhorn CDF and Quantile Operator
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
Likelihood-Free Overcomplete ICA and ApplicationsIn Causal Discovery
Interior-point Methods Strike Back: Solving the Wasserstein Barycenter Problem
Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs
Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
Efficient Non-Convex Stochastic Compositional Optimization Algorithm via Stochastic Recursive Gradient Descent
On the convergence of single-call stochastic extra-gradient methods
Infra-slow brain dynamics as a marker for cognitive function and decline
Robust Principle Component Analysis with Adaptive Neighbors
High-Quality Self-Supervised Deep Image Denoising
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup
GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs
Online Prediction of Switching Graph Labelings with Cluster Specialists
Graph-Based Semi-Supervised Learning with Non-ignorable Non-response
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs
Cross-lingual Language Model Pretraining
Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse
Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input
Universal Invariant and Equivariant Graph Neural Networks
The bias of the sample mean in multi-armed bandits can be positive or negative
On the Correctness and Sample Complexity of Inverse Reinforcement Learning
VIREL: A Variational Inference Framework for Reinforcement Learning
First Order Motion Model for Image Animation
Tensor Monte Carlo: Particle Methods for the GPU era
Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction
Learning from Label Proportions with Generative Adversarial Networks
Efficient and Thrifty Voting by Any Means Necessary
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning
Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning
Toward a Characterization of Loss Functions for Distribution Learning
Coresets for Archetypal Analysis
Emergence of Object Segmentation in Perturbed Generative Models
Optimal Sparse Decision Trees
Escaping from saddle points on Riemannian manifolds
Muti-source Domain Adaptation for Semantic Segmentation
Localized Structured Prediction
Nonzero-sum Adversarial Hypothesis Testing Games
Manifold-regression to predict from MEG/EEG brain signals without source modeling
Modeling Tabular data using Conditional GAN
Normalization Helps Training of Quantized LSTM
Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration
Deep Scale-spaces: Equivariance Over Scale
GRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series
Estimating Convergence of Markov chains with L-Lag Couplings
Learning-Based Low-Rank Approximations
Implicit Regularization in Deep Matrix Factorization
List-decodable Linear Regression
Learning elementary structures for 3D shape generation and matching
On the Hardness of Robust Classification
Foundations of Comparison-Based Hierarchical Clustering
What the Vec? Towards Probabilistically Grounded Embeddings
Minimizers of the Empirical Risk and Risk Monotonicity
Explicit Planning for Efficient Exploration in Reinforcement Learning
Lower Bounds on Adversarial Robustness from Optimal Transport
Neural Spline Flows
Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization
Nonlinear scaling of resource allocation in sensory bottlenecks
Constrained Reinforcement Learning: A Dual Approach
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
An adaptive nearest neighbor rule for classification
Coresets for Clustering with Fairness Constraints
PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments
MAVEN: Multi-Agent Variational Exploration
Competitive Gradient Descent
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
Continual Unsupervised Representation Learning
Self-Routing Capsule Networks
The Parameterized Complexity of Cascading Portfolio Scheduling
Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards
Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes
Sequence Modelling with Unconstrained Generation Order
Probabilistic Logic Neural Networks for Reasoning
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families
A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond
The Implicit Bias of AdaGrad on Separable Data
On two ways to use determinantal point processes for Monte Carlo integration
LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition
How degenerate is the parametrization of neural networks with the ReLU activation function?
Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks
Re-examination of the Role of Latent Variables in Sequence Modeling
Max-value Entropy Search for Multi-Objective Bayesian Optimization
Stein Variational Gradient Descent With Matrix-Valued Kernels
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms
Detecting Overfitting via Adversarial Examples
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies
Towards Understanding the Importance of Shortcut Connections in Residual Networks
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
Solving Interpretable Kernel Dimensionality Reduction
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space
A Model to Search for Synthesizable Molecules
Post training 4-bit quantization of convolutional networks for rapid-deployment
Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes
Differentially Private Anonymized Histograms
Dynamic Local Regret for Non-convex Online Forecasting
Learning Local Search Heuristics for Boolean Satisfiability
Provably Efficient Q-Learning with Low Switching Cost
Solving graph compression via optimal transport
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Stability of Graph Scattering Transforms
A Debiased MDI Feature Importance Measure for Random Forests
Difference Maximization Q-learning: Provably Efficient Q-learning with Function Approximation
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks
Rapid Convergence of the Unadjusted Langevin Algorithm: Log-Sobolev Suffices
Learning Distributions Generated by One-Layer ReLU Networks
Large-scale optimal transport map estimation using projection pursuit
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning
On Exact Computation with an Infinitely Wide Neural Net
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning
Chirality Nets for Human Pose Regression
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds
Fast Decomposable Submodular Function Minimization using Constrained Total Variation
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
Spherical Text Embedding
Möbius Transformation for Fast Inner Product Search on Graph
Hyperbolic Graph Neural Networks
Average Individual Fairness: Algorithms, Generalization and Experiments
Fixing the train-test resolution discrepancy
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes
Manipulating a Learning Defender and Ways to Counteract
Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations
Learning to Infer Implicit Surfaces without 3D Supervision
Fast and Accurate Least-Mean-Squares Solvers
Certifiable Robustness to Graph Perturbations
Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay
Paradoxes in Fair Machine Learning
Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost
The spiked matrix model with generative priors
Gradient Dynamics of Shallow Low-Dimensional ReLU Networks
Robust and Communication-Efficient Collaborative Learning
Multiclass Learning from Contradictions
Learning from Trajectories via Subgoal Discovery
Distributed Low-rank Matrix Factorization With Exact Consensus
Online Normalization for Training Neural Networks
The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic
An adaptive Mirror-Prox method for variational inequalities with singular operators
N-Gram Graph: A Simple Unsupervised Representation for Molecules
Characterizing the exact behaviors of temporal difference learning algorithms using Markov jump linear system theory
Facility Location Problem in Differential Privacy Model Revisited
Revisiting Auxiliary Latent Variables in Generative Models
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator
A Universally Optimal Multistage Accelerated Stochastic Gradient Method
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
Large Memory Layers with Product Keys
Learning Deterministic Weighted Automata with Queries and Counterexamples
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals
Visualizing and Measuring the Geometry of BERT
Self-Critical Reasoning for Robust Visual Question Answering
Learning to Screen
A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers
A Little Is Enough: Circumventing Defenses For Distributed Learning
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks
A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions
Finite-Sample Analysis for SARSA with Linear Function Approximation
Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models
Graph Structured Prediction Energy Networks
Private Learning Implies Online Learning: An Efficient Reduction
Graph Agreement Models for Semi-Supervised Learning
Latent distance estimation for random geometric graphs
Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network
The Functional Neural Process
Recurrent Registration Neural Networks for Deformable Image Registration
Unsupervised State Representation Learning in Atari
Unlocking Fairness: a Trade-off Revisited
Fisher Efficient Inference of Intractable Models
Thompson Sampling and Approximate Inference
PRNet: Self-Supervised Learning for Partial-to-Partial Registration
Surrogate Objectives for Batch Policy Optimization in One-step Decision Making
Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization
Approximating the Permanent by Sampling from Adaptive Partitions
Retrosynthesis Prediction with Conditional Graph Logic Network
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration
Online Learning via the Differential Privacy Lens
3D Object Detection from a Single RGB Image via Perspective Points
Parameter elimination in particle Gibbs sampling
This Looks Like That: Deep Learning for Interpretable Image Recognition
Adaptively Aligned Image Captioning via Adaptive Attention Time
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Learning Bayesian Networks with Low Rank Conditional Probability Tables
Equal Opportunity in Online Classification with Partial Feedback
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations
Neural Multisensory Scene Inference
Regret Bounds for Thompson Sampling in Restless Bandit Problems
What Can ResNet Learn Efficiently, Going Beyond Kernels?
Better Transfer Learning Through Inferred Successor Maps
Unsupervised Co-Learning on 
G
-Manifolds Across Irreducible Representations
Defending Against Neural Fake News
Sample Adaptive MCMC
A Stochastic Composite Gradient Method with Incremental Variance Reduction
Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses
STAR-Caps: Capsule Networks with Straight-Through Attentive Routing
Limitations of Lazy Training of Two-layers Neural Network
Reconciling meta-learning and continual learning with online mixtures of tasks
Distributionally Robust Optimization and Generalization in Kernel Methods
A General Theory of Equivariant CNNs on Homogeneous Spaces
Trivializations for Gradient-Based Optimization on Manifolds
Write, Execute, Assess: Program Synthesis with a REPL
A Meta-Analysis of Overfitting in Machine Learning
(Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback
Sampling Networks and Aggregate Simulation for Online POMDP Planning
Correlation in Extensive-Form Games: Saddle-Point Formulation and Benchmarks
GNNExplainer: Generating Explanations for Graph Neural Networks
Linear Stochastic Bandits Under Safety Constraints
A coupled autoencoder approach for multi-modal analysis of cell types
Towards Automatic Concept-based Explanations
A Deep Probabilistic Model for Compressing Low Resolution Videos
Budgeted Reinforcement Learning in Continuous State Space
The Discovery of Useful Questions as Auxiliary Tasks
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias
Correlation clustering with local objectives
Multiclass Performance Metric Elicitation
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
Explicit Explore-Exploit Algorithms in Continuous State Spaces
ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices
Understanding Posterior Collapse in Variational Autoencoders
Language as an Abstraction for Hierarchical Deep Reinforcement Learning
Efficient online learning with kernels for adversarial large scale problems
A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Certified Adversarial Robustness with Addition Gaussian Noise
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels
Non-Cooperative Inverse Reinforcement Learning
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization
Sobolev Independence Criterion
Maximum Entropy Monte-Carlo Planning
Learning from brains how to regularize machines
Using Statistics to Automate Stochastic Optimization
Zero-shot Knowledge Transfer via Adversarial Belief Matching
Differentiable Convex Optimization Layers
Random Tessellation Forests
Learning Nearest Neighbor Graphs from Noisy Distance Samples
Lookahead Optimizer: k steps forward, 1 step back
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Covariate-Powered Empirical Bayes Estimation
Understanding the Role of Momentum in Stochastic Gradient Methods
A neurally plausible model for online recognition andpostdiction in a dynamical environment
Guided Meta-Policy Search
Marginalized Off-Policy Evaluation for Reinforcement Learning
Contextual Bandits with Cross-Learning
Evaluating Protein Transfer Learning with TAPE
A Bayesian Theory of Conformity in Collective Decision Making
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation
A Benchmark for Interpretability Methods in Deep Neural Networks
Memory Efficient Adaptive Optimization
Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions
Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning
Systematic generalization through meta sequence-to-sequence learning
Bayesian Joint Estimation of Multiple Graphical Models
Practical Two-Step Lookahead Bayesian Optimization
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks
Neural Jump Stochastic Differential Equations
Learning metrics for persistence-based summaries and applications for graph classification
ON THE VALUE OF TARGET SAMPLING IN COVARIATE-SHIFT
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization
On Robustness of Principal Component Regression
Meta Learning with Relational Information for Short Sequences
Residual Flows for Invertible Generative Modeling
Multi-Agent Common Knowledge Reinforcement Learning
Learning to Learn By Self-Critique
Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes
Neural Networks with Cheap Differential Operators
Transductive Zero-Shot Learning with Visual Structure Constraint
Dying Experts: Efficient Algorithms with Optimal Regret Bounds
Model similarity mitigates test set overuse
A unified theory for the origin of grid cells through the lens of pattern formation
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons
Hierarchical Decision Making by Generating and Following Natural Language Instructions
SHE: A Fast and Accurate Deep Neural Network for Encrypted Data
Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond
A Game Theoretic Approach to Class-wise Selective Rationalization
Efficiently avoiding saddle points with zero order methods: No gradients required
Metamers of neural networks reveal divergence from human perceptual systems
Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization
Decentralized sketching of low rank matrices
Average Case Column Subset Selection for Entrywise 
ℓ
1
-Norm Loss
Efficient Forward Architecture Search
Unsupervised Meta Learning for Few-Show Image Classification
Learning Mixtures of Plackett-Luce Models from Structured Partial Orders
Certainty Equivalence is Efficient for Linear Quadratic Control
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models
Logarithmic Regret for Online Control
Elliptical Perturbations for Differential Privacy
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
KNG: The K-Norm Gradient Mechanism
CXPlain: Causal Explanations for Model Interpretation under Uncertainty
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning
STREETS: A Novel Camera Network Dataset for Traffic Flow
Sequential Neural Processes
Policy Continuation with Hindsight Inverse Dynamics
Learning to Self-Train for Semi-Supervised Few-Shot Classification
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization
On the Expressive Power of Deep Polynomial Neural Networks
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation
Can SGD Learn Recurrent Neural Networks with Provable Generalization?
Limits of Private Learning with Access to Public Data
Discrete Object Generation with Reversible Inductive Construction
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards
Superset Technique for Approximate Recovery in One-Bit Compressed Sensing
Bandits with Feedback Graphs and Switching Costs
Functional Adversarial Attacks
Statistical-Computational Tradeoff in Single Index Models
On Fenchel Mini-Max Learning
MarginGAN: Adversarial Training in Semi-Supervised Learning
Poincar\'{e} Recurrence, Cycles and Spurious Equilibria in Gradient Descent for Non-Convex Non-Concave Zero-Sum Games
A unified variance-reduced accelerated gradient method for convex optimization
Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin
Same-Cluster Querying for Overlapping Clusters
Efficient Convex Relaxations for Streaming PCA
Learning Robust Global Representations by Penalizing Local Predictive Power
Unsupervised Curricula for Visual Meta-Reinforcement Learning
Sample Complexity of Learning Mixture of Sparse Linear Regressions
Large Scale Adversarial Representation Learning
G2SAT: Learning to Generate SAT Formulas
Neural Proximal Policy Optimization Attains Optimal Policy
Dimensionality reduction: theoretical perspective on practical measures
Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback
Multilabel reductions: what is my loss optimising?
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks
Deep Gamblers: Learning to Abstain with Portfolio Theory
Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples
Transfer Learning via Boosting to Minimize the Performance Gap Between Domains
Splitting Steepest Descent for Progressive Training of Neural Networks
Sequential Experimental Design for Transductive Linear Bandits
Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering
Variational Graph Recurrent Neural Networks
Semi-Implicit Graph Variational Auto-Encoders
Unsupervised Learning of Object Keypoints for Perception and Control
InteractiveRecGAN: a Model Based Reinforcement Learning Method with Adversarial Training for Online Recommendation
Optimizing Generalized Rate Metrics through Three-player Games
Consistency-based Semi-supervised Learning for Object detection
Rates of Convergence for Large-scale Nearest Neighbor Classification
An Embedding Framework for Consistent Polyhedral Surrogates
Cross-Modal Learning with Adversarial Samples
Fast PAC-Bayes via Shifted Rademacher Complexity
Cell-Attention Reduces Vanishing Saliency of Recurrent Neural Networks
Program Synthesis and Semantic Parsing with Learned Code Idioms
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks
High-Dimensional Optimization in Adaptive Random Subspaces
Random Projections with Asymmetric Quantization
Superposition of many models into one
Private Testing of Distributions via Sample Permutations
McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds
How to Initialize your Network? Robust Initialization for WeightNorm & ResNets
On Making Stochastic Classifiers Deterministic
Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection
Improving Black-box Adversarial Attacks with a Transfer-based Prior
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Statistical Model Aggregation via Parameter Matching
On the (in)fidelity and sensitivity of explanations
Exponential Family Estimation via Adversarial Dynamics Embedding
The Broad Optimality of Profile Maximum Likelihood
MintNet: Building Invertible Neural Networks with Masked Convolutions
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates
On Distributed Averaging for Stochastic k-PCA
Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
Online Forecasting of Total-Variation-bounded Sequences
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization
Dynamic Curriculum Learning by Gradient Descent
Unified Sample-Optimal Property Estimation in Near-Linear Time
Region Mutual Information Loss for Semantic Segmentation
Learning Stable Deep Dynamics Models
Image Captioning: Transforming Objects into Words
Greedy Sampling for Approximate Clustering in the Presence of Outliers
Adversarial Fisher Vectors for Unsupervised Representation Learning
On Tractable Computation of Expected Predictions
Levenshtein Transformer
Unlabeled Data Improves Adversarial Robustness
Machine Teaching of Active Sequential Learners
Gaussian-Based Pooling for Convolutional Neural Networks
Meta Architecture Search
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards
Private Stochastic Convex Optimization with Optimal Rates
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Demystifying Black-box Models with Symbolic Metamodels
Neural Temporal-Difference Learning Converges to Global Optima
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces
Attentive State-Space Modeling of Disease Progression
Online EXP3 Learning in Adversarial Bandits with Delayed Feedback
A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport
Faster Boosting with Smaller Memory
Variance Reduction for Matrix Games
Learning Neural Networks with Adaptive Regularization
Distributed estimation of the inverse Hessian by determinantal averaging
Smoothing Structured Decomposable Circuits
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
Provable Non-linear Inductive Matrix Completion
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback
Sparse Variational Inference: Bayesian Coresets from Scratch
Many-Armed Bandits with High-Dimensional Contexts under a Low-Rank Structure
A Necessary and Sufficient Stability Notion for Adaptive Generalization
Necessary and Sufficient Geometries for Adaptive Gradient Algorithms
Landmark Ordinal Embedding
Identification of Conditional Causal Effects under Markov Equivalence
The Thermodynamic Variational Objective
Global Guarantees for Blind Demodulation with Generative Priors
Exact sampling of determinantal point processes with sublinear time preprocessing
Geometry-Aware Neural Rendering
Variational Temporal Abstraction
Subquadratic High-Dimensional Hierarchical Clustering
Learning Auctions with Robust Incentive Guarantees
Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games
Uniform convergence may be unable to explain generalization in deep learning
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
DTWNet: a Dynamic Time Warping Network
Structured Graph Learning Via Laplacian Spectral Constraints
Thresholding Bandit with Optimal Aggregate Regret
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks
Rethinking Kernel Methods for Node Representation Learning on Graphs
Causal Misidentification in Imitation Learning
Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data
Dimension-Free Bounds for Low-Precision Training
Concentration of risk measures: A Wasserstein distance approach
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Bayesian Optimization with Unknown Search Space
On the Downstream Performance of Compressed Word Embeddings
Multivariate Distributionally Robust Convex Regression under Absolute Error Loss
Neural Relational Inference with Fast Modular Meta-learning
Gradient based sample selection for online continual learning
Attribution-Based Confidence Metric For Deep Neural Networks
Theoretical evidence for adversarial robustness through randomization
Online Continual Learning with Maximal Interfered Retrieval
Neural Attribution for Semantic Bug-Localization in Student Programs
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
SPoC: Search-based Pseudocode to Code
Generative Modeling by Estimating Gradients of the Data Distribution
Adversarial Music: Real world Audio Adversary against Wake-word Detection System
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
Debiased Bayesian inference for average treatment effects
Margin-Based Generalization Lower Bounds for Boosted Classifiers
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio
Graph Transformer Networks
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder
The Impact of Regularization on High-dimensional Logistic Regression
Adaptive Density Estimation for Generative Models
Fast and Provable ADMM for Learning with Generative Priors
Weighted Linear Bandits for Non-Stationary Environments
Improved Regret Bounds for Bandit Combinatorial Optimization
Pareto Multi-Task Learning
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits
Novel positional encodings to enable tree-based transformers
A Domain Agnostic Measure for Monitoring and Evaluating GANs
Submodular Function Minimization with Noisy Evaluation Oracle
Counting the Optimal Solutions in Graphical Models
Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach
Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling
Bootstrapping Upper Confidence Bound
Integer Discrete Flows and Lossless Compression
Structured Prediction with Projection Oracles
Primal Dual Formulation For Deep Learning With Constraints
Screening Sinkhorn Algorithm for Regularized Optimal Transport
PAC-Bayes Un-Expected Bernstein Inequality
Are Labels Required for Improving Adversarial Robustness?
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
Multi-objective Bayesian optimisation with preferences over objectives
Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging
Calibration tests in multi-class classification: A unifying framework
Classification Accuracy Score for Conditional Generative Models
Theoretical Analysis Of Adversarial Learning: A Minimax Approach
Multiagent Evaluation under Incomplete Information
Tree-Sliced Variants of Wasserstein Distances
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration
Comparing distributions: 
ℓ
1
 geometry improves kernel two-sample testing
Robustness Verification of Tree-based Models
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents
Fast and Accurate Stochastic Gradient Estimation
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning
Root Mean Square Layer Normalization
Universality in Learning from Linear Measurements
Planning in Entropy-Regularized Markov Decision Processes and Games
Exponentially convergent stochastic k-PCA without variance reduction
R2D2: Reliable and Repeatable Detectors and Descriptors for Joint Sparse Keypoint Detection and Local Feature Extraction
Selective Sampling-based Scalable Sparse Subspace Clustering
A General Framework for Efficient Symmetric Property Estimation
Structured Variational Inference in Continuous Cox Process Models
Generalization of Reinforcement Learners with Working and Episodic Memory
Distribution Learning of a Random Spatial Field with a Location-Unaware Mobile Sensor
Hindsight Credit Assignment
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets
Kernelized Bayesian Softmax for Text Generation
When to Trust Your Model: Model-Based Policy Optimization
Correlation Clustering with Adaptive Similarity Queries
Control What You Can: Intrinsically Motivated Task-Planning Agent
Selecting causal brain features with a single conditional independence test per feature
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
A Generic Acceleration Framework for Stochastic Composite Optimization
Beating SGD Saturation with Tail-Averaging and Minibatching
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond
Continuous-time Models for Stochastic Optimization Algorithms
Curriculum-guided Hindsight Experience Replay
Implicit Semantic Data Augmentation for Deep Networks
MetaInit: Initializing learning by learning to initialize
Scalable Deep Generative Relational Model with High-Order Node Dependence
Random Path Selection for Continual Learning
Efficient Algorithms for Smooth Minimax Optimization
Shadowing Properties of Optimization Algorithms
Causal Regularization
Learning Hawkes Processes from a handful of events
Unsupervised Object Segmentation by Redrawing
Regret Bounds for Learning State Representations in Reinforcement Learning
Band-Limited Gaussian Processes: The Sinc Kernel
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Feedforward Bayesian Inference for Crowdsourced Classification
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
k-Means Clustering of Lines for Big Data
Random projections and sampling algorithms for clustering of high-dimensional polygonal curves
Recurrent Space-time Graph Neural Networks
Uncertainty on Asynchronous Event Prediction
Accurate, reliable and fast robustness evaluation
Sparse High-Dimensional Isotonic Regression
Triad Constraints for Learning Causal Structure of Latent Variables
On the Inductive Bias of Neural Tangent Kernels
Cross-Domain Transferable Perturbations
Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices
Kernel quadrature with DPPs
REM: From Structural Entropy to Community Structure Deception
Sim2real transfer learning for 3D pose estimation: motion to the rescue
Self-Supervised Deep Learning on Point Clouds by Reconstructing Space
Piecewise Strong Convexity of Neural Networks
Minimum Stein Discrepancy Estimators
Fast and Furious Learning in Zero-Sum Games: Vanishing Regret with Non-Vanishing Step Sizes
Generalization Bounds for Neural Networks via Approximate Description Length
Provably robust boosted decision stumps and trees against adversarial attacks
Convergence of Adversarial Training in Overparametrized Neural Networks
A Composable Specification Language for Reinforcement Learning Tasks
The Option Keyboard: Combining Skills in Reinforcement Learning
Unified Language Model Pre-training for Natural Language Understanding and Generation
Learning to Correlate in Multi-Player General-Sum Sequential Games
Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match
Generative Well-intentioned Networks
Online-Within-Online Meta-Learning
Learning step sizes for unfolded sparse coding
Biases for Emergent Communication in Multi-agent Reinforcement Learning
Episodic Memory in Lifelong Language Learning
A Simple Baseline for Bayesian Uncertainty in Deep Learning
Communication-efficient Distributed SGD with Sketching
Modeling Conceptual Understanding in Image Reference Games
Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights
Near Neighbor: Who is the Fairest of Them All?
Outlier-robust estimation of a sparse linear model using 
ℓ
1
-penalized Huber's 
M
-estimator
Learning nonlinear level sets for dimensionality reduction in function approximation
Assessing Social and Intersectional Biases in Contextualized Word Representations
Online Convex Matrix Factorization with Representative Regions
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game
Simultaneous Matching and Ranking as end-to-end Deep Classification: A Case study of Information Retrieval with 50M Documents
A Fourier Perspective on Model Robustness in Computer Vision
The continuous Bernoulli: fixing a pervasive error in variational autoencoders
Privacy Amplification by Mixing and Diffusion Mechanisms
Variance Reduction in Bipartite Experiments through Correlation Clustering
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
Metalearned Neural Memory
Learning Multiple Markov Chains via Adaptive Allocation
Diffusion Improves Graph Learning
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data
Variational Bayes under Model Misspecification
On the Importance of Initialization in Optimization for Deep Linear Neural Networks
On Differentially Private Graph Sparsification and Applications
Manifold denoising by Nonlinear Robust Principal Component Analysis
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
Optimal Sampling and Clustering in the Stochastic Block Model
Recurrent Kernel Networks
Cold Case: The Lost MNIST Digits
Hierarchical Optimal Transport for Multimodal Distribution Alignment
Exploration via Hindsight Goal Generation
Shaping Belief States with Generative Environment Models for RL
Globally Optimal Learning for Structured Elliptical Losses
Object landmark discovery through unsupervised adaptation
Specific and Shared Causal Relation Modeling and Mechanism-based Clustering
Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions
RUDDER: Return Decomposition for Delayed Rewards
Graph Normalizing Flows
Explanations can be manipulated and geometry is to blame
Communication trade-offs for synchronized distributed SGD with large step size
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
No-Regret Learning in Unknown Games with Correlated Payoffs
Alleviating Label Switching with Optimal Transport
Paraphrase Generation with Latent Bag of Words
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Compacting, Picking and Growing for Unforgetting Continual Learning
Approximating Interactive Human Evaluation withSelf-Play for Open-Domain Dialog Systems
A New Distribution on the Simplex with Auto-Encoding Applications
AutoPrun: Automatic Network Pruning by Regularizing Auxiliary Parameters
A neurally plausible model learns successor representations in partially observable environments
Learning about an exponential amount of conditional distributions
Towards modular and programmable architecture search
Towards Hardware-Aware Tractable Learning of Probabilistic Models
On Robustness to Adversarial Examples and Polynomial Optimization
Rand-NSG: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node
A Solvable High-Dimensional Model of GAN
Using Embeddings to Correct for Unobserved Confounding in Networks
PolyTree framework for tree ensemble analysis
Bayesian Optimization under Heavy-tailed Payoffs
Combining Generative and Discriminative Models for Hybrid Inference
A Graph Theoretic Additive Approximation of Optimal Transport
Adversarial Robustness through Local Linearization
Sampled softmax with random Fourier features
Semi-flat minima and saddle points by embedding neural networks to overparameterization
Learning Fairness in Multi-Agent Systems
Primal-Dual Block Frank-Wolfe
GOT: An Optimal Transport framework for Graph comparison
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
Complexity of Highly Parallel Non-Smooth Convex Optimization
Inverting Deep Generative models, One layer at a time
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
The Implicit Metropolis-Hastings Algorithm
An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift
Accurate Layerwise Interpretable Competence Estimation
A New Perspective on Pool-Based Active Classification and False-Discovery Control
A First-Order Approach to Accelerated Value Iteration
Defending Neural Backdoors via Generative Distribution Modeling
Are Sixteen Heads Really Better than One?
Multi-resolution Multi-task Gaussian Processes
Variational Bayesian Optimal Experimental Design
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes
Reinforcement Learning with Convex Constraints
User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning
Stochastic Bandits with Context Distributions
Inducing brain-relevant bias in natural language processing models
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
Recovering Bandits
Computing Linear Restrictions of Neural Networks
Learning Positive Functions with Pseudo Mirror Descent
Correlation Priors for Reinforcement Learning
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression
A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit
Differentially Private Covariance Estimation
Outlier Detection and Robust PCA Using a Convex Measure of Innovation
Integrating mechanistic and structural causal models enables counterfactual inference in complex systems
Are Disentangled Representations Helpful for Abstract Visual Reasoning?
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
Stochastic Frank-Wolfe for Composite Convex Minimization
Consistent Constraint-Based Causal Structure Learning
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis
Sample Efficient Active Learning of Causal Trees
Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection
Robust Attribution Regularization
Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization
When to use parametric models in reinforcement learning?
General E(2)-Equivariant Steerable CNNs
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
Structure Learning with Side Information: Sample Complexity
Untangling in Invariant Speech Recognition
Flexible information routing in neural populations through stochastic comodulation
Generalization Bounds in the Predict-then-Optimize Framework
Categorized Bandits
Worst-Case Regret Bounds for Exploration via Randomized Value Functions
Efficient characterization of electrically evoked responses for neural interfaces
Differentially Private Distributed Data Summarization under Covariate Shift
Hamiltonian descent for composite objectives
Implicit Regularization of Accelerated Methods in Hilbert Spaces
Non-Asymptotic Pure Exploration by Solving Games
Implicit Posterior Variational Inference for Deep Gaussian Processes
Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces
Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback
Cormorant: Covariant Molecular Neural Networks
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
Reflection Separation using a Pair of Unpolarized and Polarized Images
Policy Poisoning in Batch Reinforcement Learning and Control
Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees
Pure Exploration with Multiple Correct Answers
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets
On the Benefits of Disentangled Representations
Compiler Auto-Vectorization using Imitation Learning
A Generalized Algorithm for Multi-Objective RL and Policy Adaptation
Exact Gaussian Processes on a Million Data Points
Bayesian Layers: A Module for Neural Network Uncertainty
Learning Compositional Neural Programs with Recursive Tree Search and Planning
Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations
Likelihood Ratios for Out-of-Distribution Detection
Discrete Flows: Invertible Generative Models of Discrete Data
Mindreader: A Self Validation Network for Object-Level Human Attention Reasoning
Model Selection for Contextual Bandits
Sliced Gromov-Wasserstein
Towards Practical Alternating Least-Squares for CCA
Deep Leakage from Gradients
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks
Value Function in Frequency Domain and Characteristic Value Iteration
Icebreaker: Efficient Information Acquisition with Active Learning
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors
Planning with Goal-Conditioned Policies
Don't take it lightly: Phasing optical random projections with unknown operators
Generating Diverse High-Fidelity Images with VQVAE-2
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs
Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis
Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
Offline Contextual Bandits with High Probability Fairness Guarantees
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)
Function-Space Distributions over Kernels
SGD for Least Squares Regression: Towards Minimax Optimality with the Final Iterate
Compositional Plan Vectors
Locally Private Learning without Interaction Requires Separation
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences
Computational Separations between Sampling and Optimization
Surfing: Iterative Optimization Over Incrementally Trained Deep Networks
Population-based Meta-Optimizer Guided by Posterior Estimation
On Human-Aligned Risk Minimization
Semi-Parametric Efficient Policy Learning with Continuous Actions
Multi-task Learning for Aggregated Data using Gaussian Processes
Minimal Variance Sampling in Stochastic Gradient Boosting
Precise and Scalable Convex Relaxations for Robustness Certification
An Algorithm to Learn Polytree Networks with Hidden Nodes
Efficiently Learning Fourier Sparse Set Functions
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions
Invariance and identifiability issues for word embeddings
Generalization Error Analysis of Quantized Compressive Learning
Multi-Criteria Dimensionality Reduction with Applications to Fairness
Efficient Rematerialization for Deep Networks
Fast Agent Resetting in Training
Heterogeneous Treatment Effects with Instruments
Understanding Sparse JL for Feature Hashing
Constraint Augmented Reinforcement Learning for Text-based Recommendation and Generation
Flexible Modeling of Diversity with Strongly Log-Concave Distributions
Momentum-Based Variance Reduction in Non-Convex SGD
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning
Can Unconditional Language Models Recover Arbitrary Sentences?
Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness
Faster width-dependent algorithm for mixed packing and covering LPs
Flattening a Hierarchical Clustering through Active Learning
DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging
Certifying Geometric Robustness of Neural Networks
Goal-conditioned Imitation Learning
Robust exploration in linear quadratic reinforcement learning
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration
Input-Output Equivalence of Unitary and Contractive RNNs
Hamiltonian Neural Networks
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
Deep and Structured Similarity Matching via Deep and Structured Hebbian/Anti-Hebbian Networks
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
Multiple Futures Prediction
Explicitly disentangling image content from translation and rotation with spatial-VAE
A Perspective on False Discovery Rate Control via Knockoffs
A Kernel Loss for Solving the Bellman Equation
Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing
Differential Privacy Has Disparate Impact on Model Accuracy
Riemannian batch normalization for SPD neural networks
Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity
Stacked Capsule Autoencoders
Learning Reward Machines for Partially Observable Reinforcement Learning
Learning Representations by Maximizing Mutual Information Across Views
Learning Deep MRFs with Amortized Bethe Free Energy Minimization
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Fast structure learning with modular regularization
Wasserstein Dependency Measure for Representation Learning
TAB-VCR: Tags and Attributes for Visual Commonsense Reasoning
Universality and individuality in neural dynamics across large populations of recurrent networks
End-to-End Learning on 3D Protein Structure for Interface Prediction
A Family of Robust Stochastic Operators for Reinforcement Learning
Improving Model Robustness and Uncertainty Estimates with Self-Supervised Learning
Inherent Tradeoffs in Learning Fair Representation
Are deep ResNets provably better than linear predictors?
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models
Gradient-based Adaptive Markov Chain Monte Carlo
On the Role of Inductive Bias From Simulation and the Transfer to the Real World: a new Disentanglement Dataset
Imitation-Projected Policy Gradient for Programmatic Reinforcement Learning
Learning Data Manipulation for Augmentation and Weighting
Exploring Algorithmic Fairness in Robust Graph Covering Problems
Abstraction based Output Range Analysis for Neural Networks
Space and Time Efficient Kernel Density Estimation in High Dimensions
PIDForest: Anomaly Detection and Certification via Partial Identification
Generative Models for Graph-Based Protein Design
The Geometry of Deep Networks: Power Diagram Subdivision
Approximate Feature Collisions in Neural Nets
Ease-of-Teaching and Language Structure from Emergent Communication
Generalization in multitask deep neural classifiers: a statistical physics approach
Distributionally Optimistic Optimization Approach to Nonparametric Likelihood Approximation
On Relating Explanations and Adversarial Examples
On the equivalence between graph isomorphism testing and function approximation with GNNs
Surround Modulation: A Bio-inspired Connectivity Structure for Convolutional Neural Networks
Self-attention with Functional Time Representation Learning
Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling
Enabling hyperparameter optimization in sequential autoencoders for spiking neural data