nips2019_preview.txt

Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation

ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
http://arxiv.org/pdf/1908.02265v1

Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers
http://arxiv.org/pdf/1905.10630v1

Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video
http://arxiv.org/pdf/1908.10553v1

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
http://arxiv.org/pdf/1902.03245v1

Stand-Alone Self-Attention in Vision Models
http://arxiv.org/pdf/1906.05909v1

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
http://arxiv.org/pdf/1909.04630v1

Adversarial Examples Are Not Bugs, They Are Features

Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks
http://arxiv.org/pdf/1907.03395v2

FreeAnchor: Learning to Match Anchors for Visual Object Detection
http://arxiv.org/pdf/1909.02466v1

Differentially Private Hypothesis Selection

New Differentially Private Algorithms for Learning Mixtures of Well-Separated Gaussians
http://arxiv.org/pdf/1909.03951v1

Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation
http://arxiv.org/pdf/1906.02830v1

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
http://arxiv.org/pdf/1905.00877v5

Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement

Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
http://arxiv.org/pdf/1906.04516v1

Generalized Sliced Wasserstein Distances
http://arxiv.org/pdf/1902.00434v1

First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
http://arxiv.org/pdf/1906.09069v1

Blind Super-Resolution Kernel Estimation using an Internal-GAN
http://arxiv.org/pdf/1909.06581v3

Noise-tolerant fair classification
http://arxiv.org/pdf/1901.10837v2

Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection
http://arxiv.org/pdf/1908.07882v3

Joint-task Self-supervised Learning for Temporal Correspondence
http://arxiv.org/pdf/1909.11895v1

Provable Gradient Variance Guarantees for Black-Box Variational Inference
http://arxiv.org/pdf/1906.08241v1

Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation
http://arxiv.org/pdf/1906.10115v1

Experience Replay for Continual Learning

Deep ReLU Networks Have Surprisingly Few Activation Patterns
http://arxiv.org/pdf/1906.00904v1

Chasing Ghosts: Instruction Following as Bayesian State Tracking
http://arxiv.org/pdf/1907.02022v1

Block Coordinate Regularization by Denoising
http://arxiv.org/pdf/1905.05113v2

Reducing Noise in GAN Training with Variance Reduced Extragradient
http://arxiv.org/pdf/1904.08598v2

Learning Erdos-Renyi Random Graphs via Edge Detecting Queries
http://arxiv.org/pdf/1905.03410v2

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
http://arxiv.org/pdf/1906.00817v1

Metric Learning for Adversarial Robustness

DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
http://arxiv.org/pdf/1905.10711v1

Batched Multi-armed Bandits Problem
http://arxiv.org/pdf/1904.01763v2

vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
http://arxiv.org/pdf/1906.07159v2

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
http://arxiv.org/pdf/1905.10874v1

Trust Region-Guided Proximal Policy Optimization
http://arxiv.org/pdf/1901.10314v1

Adversarial Self-Defense for Cycle-Consistent GANs
http://arxiv.org/pdf/1908.01517v1

Towards closing the gap between the theory and practice of SVRG
http://arxiv.org/pdf/1908.02725v1

Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
http://arxiv.org/pdf/1906.01376v1

ETNet: Error Transition Network for Arbitrary Style Transfer

No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms
http://arxiv.org/pdf/1906.11389v1

Deep Equilibrium Models
http://arxiv.org/pdf/1909.01377v1

Saccader: Accurate, Interpretable Image Classification with Hard Attention

Multiway clustering via tensor block models
http://arxiv.org/pdf/1906.03807v2

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
http://arxiv.org/pdf/1905.09717v2

Differentiable Cloth Simulation for Inverse Problems

Poisson-randomized Gamma Dynamical Systems

Volumetric Correspondence Networks for Optical Flow

Learning Conditional Deformable Templates with Convolutional Networks
http://arxiv.org/pdf/1908.02738v1

Fast Low-rank Metric Learning for Large-scale and High-dimensional Data
http://arxiv.org/pdf/1909.06297v1

Efficient Symmetric Norm Regression via Linear Sketching

RUBi: Reducing Unimodal Biases in Visual Question Answering
http://arxiv.org/pdf/1906.10169v1

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
http://arxiv.org/pdf/1909.02253v1

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
http://arxiv.org/pdf/1905.11394v2

Efficient 3D Deep Learning via Point-Based Representation and Voxel-Based Convolution

Deep Learning without Weight Transport
http://arxiv.org/pdf/1904.05391v3

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
http://arxiv.org/pdf/1906.11881v1

Polynomial Cost of Adaptation for X-Armed Bandits
http://arxiv.org/pdf/1905.10221v1

Learning to Propagate for Graph Meta-Learning
http://arxiv.org/pdf/1909.05024v1

Secretary Ranking with Minimal Inversions
http://arxiv.org/pdf/1811.06444v1

Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes

Learning Perceptual Inference by Contrasting

Selecting the independent coordinates of manifolds with large aspect ratios
http://arxiv.org/pdf/1907.01651v2

Region-specific Diffeomorphic Metric Mapping
http://arxiv.org/pdf/1906.00139v1

Subset Selection via Supervised Facility Location

Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations
http://arxiv.org/pdf/1906.01618v1

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
http://arxiv.org/pdf/1905.03814v1

A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation
http://arxiv.org/pdf/1905.11722v1

Combinatorial Inference against Label Noise

Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning
http://arxiv.org/pdf/1901.09326v3

Convolution with even-sized kernels and symmetric padding
http://arxiv.org/pdf/1903.08385v2

On The Classification-Distortion-Perception Tradeoff
http://arxiv.org/pdf/1904.08816v1

Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up
http://arxiv.org/pdf/1905.03135v1

Online sampling from log-concave distributions
http://arxiv.org/pdf/1902.08179v3

Envy-Free Classification
http://arxiv.org/pdf/1809.08700v1

Finding Friend and Foe in Multi-Agent Games
http://arxiv.org/pdf/1906.02330v1

Computer Vision with a Single (Robust) Classifier

Gated CRF Loss for Weakly Supervised Semantic Image Segmentation
http://arxiv.org/pdf/1906.04651v1

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
http://arxiv.org/pdf/1907.02690v3

Conditional Structure Generation through Graph Variational Generative Adversarial Nets

Distributional Policy Optimization: An Alternative Approach for Continuous Control
http://arxiv.org/pdf/1905.09855v1

Sampling Sketches for Concave Sublinear Functions of Frequencies
http://arxiv.org/pdf/1907.02218v1

Deliberative Explanations: visualizing network insecurities

Computing Full Conformal Prediction Set with Approximate Homotopy
http://arxiv.org/pdf/1909.09365v1

Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
http://arxiv.org/pdf/1810.11953v3

Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards

Multi-View Reinforcement Learning

Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution
http://arxiv.org/pdf/1909.06720v1

Neural Diffusion Distance for Image Segmentation

Fine-grained Optimization of Deep Neural Networks
http://arxiv.org/pdf/1905.09054v1

Extending Stein’s Unbiased Risk Estimator To Train Deep Denoisers with Correlated Pairs of Noisy Images
http://arxiv.org/pdf/1902.02452v2

Wibergian Learning of Continuous Energy Functions

Hyperspherical Prototype Networks
http://arxiv.org/pdf/1901.10514v2

Expressive power of tensor-network factorizations for probabilistic modelling

HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs
http://arxiv.org/pdf/1809.02589v4

SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points
http://arxiv.org/pdf/1904.09265v2

Efficient Meta Learning via Minibatch Proximal Update

Unconstrained Monotonic Neural Networks
http://arxiv.org/pdf/1908.05164v2

Guided Similarity Separation for Image Retrieval

Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
http://arxiv.org/pdf/1906.07413v1

Strategizing against No-regret Learners

D-VAE: A Variational Autoencoder for Directed Acyclic Graphs

Hierarchical Optimal Transport for Document Representation
http://arxiv.org/pdf/1906.10827v1

Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes

Positional Normalization
http://arxiv.org/pdf/1907.04312v1

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
http://arxiv.org/pdf/1811.10745v2

Incremental Scene Synthesis
http://arxiv.org/pdf/1811.12297v3

Self-Supervised Generalisation with Meta Auxiliary Learning
http://arxiv.org/pdf/1901.08933v2

Variational Denoising Network: Toward Blind Noise Modeling and Removal
http://arxiv.org/pdf/1908.11314v2

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
http://arxiv.org/pdf/1812.04529v1

On the Curved Geometry of Accelerated Optimization
http://arxiv.org/pdf/1812.04634v1

Multi-marginal Wasserstein GAN

Better Exploration with Optimistic Actor Critic

Importance Resampling for Off-policy Prediction
http://arxiv.org/pdf/1906.04328v1

The Label Complexity of Active Learning from Observational Data
http://arxiv.org/pdf/1905.12791v1

Meta-Learning Representations for Continual Learning

Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training
http://arxiv.org/pdf/1907.10764v3

Visualizing the PHATE of Neural Networks
http://arxiv.org/pdf/1908.02831v1

The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers
http://arxiv.org/pdf/1906.07282v1

Nonconvex Low-Rank Tensor Completion from Noisy Data

Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization
http://arxiv.org/pdf/1905.12776v2

Channel Gating Neural Networks
http://arxiv.org/pdf/1805.12549v1

Neural networks grown and self-organized by noise
http://arxiv.org/pdf/1906.01039v1

Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning

Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
http://arxiv.org/pdf/1902.07379v5

Variational Structured Semantic Inference for Diverse Image Captioning

Mapping State Space using Landmarks for Universal Goal Reaching
http://arxiv.org/pdf/1908.05451v1

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
http://arxiv.org/pdf/1906.08031v1

CNN^{2}: Viewpoint Generalization via a Binocular Vision

Generalized Off-Policy Actor-Critic
http://arxiv.org/pdf/1903.11329v6

DAC: The Double Actor-Critic Architecture for Learning Options
http://arxiv.org/pdf/1904.12691v7

Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models

Controlling Neural Level Sets
http://arxiv.org/pdf/1905.11911v1

Blended Matching Pursuit
http://arxiv.org/pdf/1904.12335v2

An Improved Analysis of Training Over-parameterized Deep Neural Networks
http://arxiv.org/pdf/1906.04688v1

Controllable Text to Image Generation
http://arxiv.org/pdf/1909.07083v1

Improving Textual Network Learning with Variational Homophilic Embeddings

Rethinking Generative Coverage: A Pointwise Guaranteed Approach
http://arxiv.org/pdf/1902.04697v6

The Randomized Midpoint Method for Log-Concave Sampling
http://arxiv.org/pdf/1909.05503v1

Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update
http://arxiv.org/pdf/1805.12375v2

Fully Neural Network based Model for General Temporal Point Processes
http://arxiv.org/pdf/1905.09690v1

Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks
http://arxiv.org/pdf/1909.08174v1

Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design

Provably Powerful Graph Networks
http://arxiv.org/pdf/1905.11136v2

Order Optimal One-Shot Distributed Learning

Information Competing Process for Learning Diversified Representations
http://arxiv.org/pdf/1906.01288v2

GENO -- GENeric Optimization for Classical Machine Learning
http://arxiv.org/pdf/1905.13587v1

Conditional Independence Testing using Generative Adversarial Networks
http://arxiv.org/pdf/1907.04068v1

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
http://arxiv.org/pdf/1906.09777v2

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
http://arxiv.org/pdf/1909.05742v1

Integrating Generative and Discriminative Sparse Kernel Machines for Multi-class Active Learning

Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
http://arxiv.org/pdf/1902.05546v1

Perceiving the arrow of time in autoregressive motion

DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
http://arxiv.org/pdf/1906.04733v1

Hyper-Graph-Network Decoders for Block Codes
http://arxiv.org/pdf/1909.09036v1

Large Scale Markov Decision Processes with Changing Rewards
http://arxiv.org/pdf/1905.10649v1

Multiview Aggregation for Learning Category-Specific Shape Reconstruction
http://arxiv.org/pdf/1907.01085v1

Semi-Parametric Dynamic Contextual Pricing
http://arxiv.org/pdf/1901.02045v4

Nearly Linear-Time, Deterministic Algorithm for Maximizing (Non-Monotone) Submodular Functions Under Cardinality Constraint
http://arxiv.org/pdf/1902.06179v2

Initialization of ReLUs for Dynamical Isometry
http://arxiv.org/pdf/1806.06362v2

Gradient Information for Representation and Modeling

SpiderBoost and Momentum: Faster Variance Reduction Algorithms

Minimax rates of estimating approximate differential privacy
http://arxiv.org/pdf/1905.10335v1

Backprop with Approximate Activations for Memory-efficient Network Training
http://arxiv.org/pdf/1901.07988v1

Training Image Estimators without Image Ground Truth
http://arxiv.org/pdf/1906.05775v1

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
http://arxiv.org/pdf/1906.02120v1

Learning Transferable Graph Exploration

Conformal Prediction Under Covariate Shift
http://arxiv.org/pdf/1904.06019v2

Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation

Asymmetric Valleys: Beyond Sharp and Flat Local Minima
http://arxiv.org/pdf/1902.00744v2

Positive-Unlabeled Compression on the Cloud
http://arxiv.org/pdf/1909.09757v1

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
http://arxiv.org/pdf/1812.03934v3

Learning Robust Options by Conditional Value at Risk Optimization
http://arxiv.org/pdf/1905.09191v3

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
http://arxiv.org/pdf/1903.10203v1

Discovering Neural Wirings
http://arxiv.org/pdf/1906.00586v3

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
http://arxiv.org/pdf/1905.07900v1

One-Shot Object Detection with Co-Attention and Co-Excitation

Quaternion Knowledge Graph Embeddings
http://arxiv.org/pdf/1904.10281v2

Glyce: Glyph-vectors for Chinese Character Representations
http://arxiv.org/pdf/1901.10125v4

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
http://arxiv.org/pdf/1906.05110v2

On the Global Convergence of (Fast) Incremental Expectation Maximization Methods

A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization
http://arxiv.org/pdf/1905.07996v1

Regularizing Trajectory Optimization with Denoising Autoencoders
http://arxiv.org/pdf/1903.11981v2

Learning Hierarchical Priors in VAEs
http://arxiv.org/pdf/1905.04982v4

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
http://arxiv.org/pdf/1902.02041v2

On Lazy Training in Differentiable Programming
http://arxiv.org/pdf/1812.07956v4

Quality Aware Generative Adversarial Networks

Copula-like Variational Inference
http://arxiv.org/pdf/1904.07153v1

Implicit Regularization for Optimal Sparse Recovery
http://arxiv.org/pdf/1909.05122v1

Locally Private Gaussian Estimation
http://arxiv.org/pdf/1811.08382v1

Multi-mapping Image-to-Image Translation via Learning Disentanglement
http://arxiv.org/pdf/1909.07877v1

Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
http://arxiv.org/pdf/1907.08350v1

Structured Decoding for Non-Autoregressive Machine Translation

Learning Temporal Pose Estimation from Sparsely-Labeled Videos
http://arxiv.org/pdf/1906.04016v1

Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning
http://arxiv.org/pdf/1905.11786v1

Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
http://arxiv.org/pdf/1905.07645v4

Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition

Real-Time Reinforcement Learning

Robust Multi-agent Counterfactual Prediction
http://arxiv.org/pdf/1904.02235v1

Approximate Inference Turns Deep Networks into Gaussian Processes
http://arxiv.org/pdf/1906.01930v1

Deep Signatures
http://arxiv.org/pdf/1905.08494v1

Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits
http://arxiv.org/pdf/1907.03346v2

Convergent Policy Optimization for Safe Reinforcement Learning

Augmented Neural ODEs
http://arxiv.org/pdf/1904.01681v2

Thompson Sampling for Multinomial Logit Contextual Bandits

Backpropagation-Friendly Eigendecomposition
http://arxiv.org/pdf/1906.09023v2

FastSpeech: Fast, Robust and Controllable Text to Speech
http://arxiv.org/pdf/1905.09263v4

Ultrametric Fitting by Gradient Descent
http://arxiv.org/pdf/1905.10566v1

Distinguishing Distributions When Samples Are Strategically Transformed

Implicit Regularization of Discrete Gradient Dynamics in Deep Linear Neural Networks

Deep Set Prediction Networks
http://arxiv.org/pdf/1906.06565v1

DppNet: Approximating Determinantal Point Processes with Deep Networks
http://arxiv.org/pdf/1901.02051v1

Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
http://arxiv.org/pdf/1909.02682v1

Neural Lyapunov Control

Fully Dynamic Consistent Facility Location

A Stickier Benchmark for General-Purpose Language Understanding Systems
http://arxiv.org/pdf/1905.00537v2

A Flexible Generative Framework for Graph-based Semi-supervised Learning
http://arxiv.org/pdf/1905.10769v1

Self-normalization in Stochastic Neural Networks

Optimal Decision Tree with Noisy Outcomes

Meta-Curvature
http://arxiv.org/pdf/1902.03356v2

Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning
http://arxiv.org/pdf/1906.03735v1

KerGM: Kernelized Graph Matching

Transfusion: Understanding Transfer Learning for Medical Imaging
http://arxiv.org/pdf/1902.07208v2

Adversarial training for free!
http://arxiv.org/pdf/1904.12843v1

Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients
http://arxiv.org/pdf/1909.07588v1

Implicitly learning to reason in first-order logic
http://arxiv.org/pdf/1906.10106v1

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
http://arxiv.org/pdf/1906.01552v1

The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric
http://arxiv.org/pdf/1902.05826v2

HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models
http://arxiv.org/pdf/1904.01121v3

First order expansion of convex regularized estimators

Capacity Bounded Differential Privacy
http://arxiv.org/pdf/1907.02159v1

Universal Boosting Variational Inference
http://arxiv.org/pdf/1906.01235v1

SGD on Neural Networks Learns Functions of Increasing Complexity

The Landscape of Non-convex Empirical Risk with Degenerate Population Risk
http://arxiv.org/pdf/1907.05520v1

Making AI Forget You: Data Deletion in Machine Learning
http://arxiv.org/pdf/1907.05012v1

Practical Differentially Private Top-k Selection with Pay-what-you-get Composition
http://arxiv.org/pdf/1905.04273v2

Conformalized Quantile Regression
http://arxiv.org/pdf/1905.03222v1

Thompson Sampling with Information Relaxation Penalties
http://arxiv.org/pdf/1902.04251v1

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
http://arxiv.org/pdf/1905.01067v3

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
http://arxiv.org/pdf/1906.04356v1

Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD
http://arxiv.org/pdf/1810.04723v3

Asymptotics for Sketching in Least Squares Regression

MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
http://arxiv.org/pdf/1905.09808v1

Exact inference in structured prediction
http://arxiv.org/pdf/1906.00451v1

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
http://arxiv.org/pdf/1905.09997v1

Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
http://arxiv.org/pdf/1909.04570v1

Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation

Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy
http://arxiv.org/pdf/1905.10477v1

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
http://arxiv.org/pdf/1906.04392v1

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
http://arxiv.org/pdf/1905.13425v1

Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG

Compression with Flows via Local Bits-Back Coding
http://arxiv.org/pdf/1905.08500v2

Exact Rate-Distortion in Autoencoders via Echo Noise
http://arxiv.org/pdf/1904.07199v2

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
http://arxiv.org/pdf/1904.06991v2

A First-order Algorithmic Framework for Distributionally Robust Logistic Regression

PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph
http://arxiv.org/pdf/1905.01608v1

Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise

Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
http://arxiv.org/pdf/1902.07565v1

Learning Generalizable Device Placement Algorithms for Distributed Machine Learning
http://arxiv.org/pdf/1906.08879v1

Uncoupled Regression from Pairwise Comparison Data
http://arxiv.org/pdf/1905.13659v2

Cross Attention Network for Few-shot Classification

A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution
http://arxiv.org/pdf/1908.10776v2

SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models
http://arxiv.org/pdf/1906.03951v1

Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs
http://arxiv.org/pdf/1901.09715v2

Teaching Multiple Concepts to a Forgetful Learner
http://arxiv.org/pdf/1805.08322v3

Regularized Weighted Low Rank Approximation

Practical and Consistent Estimation of f-Divergences

Approximation Ratios of Graph Neural Networks for Combinatorial Problems
http://arxiv.org/pdf/1905.10261v1

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
http://arxiv.org/pdf/1901.10275v2

Full-Gradient Representation for Neural Network Visualization
http://arxiv.org/pdf/1905.00780v3

q-means: A quantum algorithm for unsupervised machine learning

Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
http://arxiv.org/pdf/1906.00429v1

Limitations of the empirical Fisher approximation
http://arxiv.org/pdf/1905.12558v1

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
http://arxiv.org/pdf/1909.09020v1

Understanding attention in graph neural networks

Data Cleansing for Models Trained with SGD
http://arxiv.org/pdf/1906.08473v1

Curvilinear Distance Metric Learning

Semantically-Regularized Logic Graph Embeddings
http://arxiv.org/pdf/1909.01161v3

Modeling Uncertainty by Learning A Hierarchy of Deep Neural Connections
http://arxiv.org/pdf/1905.13195v1

Efficient Graph Generation with Graph Recurrent Attention Networks

Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms
http://arxiv.org/pdf/1905.09050v1

Learning Deep Bilinear Transformation for Fine-grained Image Representation

Practical Deep Learning with Bayesian Principles
http://arxiv.org/pdf/1906.02506v1

Training Language GANs from Scratch
http://arxiv.org/pdf/1905.09922v1

Pseudo-Extended Markov chain Monte Carlo
http://arxiv.org/pdf/1708.05239v2

Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate

Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters

On Adversarial Mixup Resynthesis
http://arxiv.org/pdf/1903.02709v3

A Geometric Perspective on Optimal Representations for Reinforcement Learning
http://arxiv.org/pdf/1901.11530v2

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
http://arxiv.org/pdf/1905.11614v1

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
http://arxiv.org/pdf/1812.05189v2

Double Quantization for Communication-Efficient Distributed Optimization
http://arxiv.org/pdf/1805.10111v4

Globally optimal score-based learning of directed acyclic graphs in high-dimensions

Multi-relational Poincaré Graph Embeddings

No-Press Diplomacy: Modeling Multi-Agent Gameplay
http://arxiv.org/pdf/1909.02128v1

State Aggregation Learning from Markov Transition Data
http://arxiv.org/pdf/1811.02619v1

Disentangling Influence: Using disentangled representations to audit model predictions
http://arxiv.org/pdf/1906.08652v1

Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
http://arxiv.org/pdf/1810.06530v3

Partially Encrypted Deep Learning using Functional Encryption
http://arxiv.org/pdf/1905.10214v3

Decentralized Cooperative Stochastic Bandits

Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
http://arxiv.org/pdf/1905.11882v2

Efficient Deep Approximation of GMMs

Learning low-dimensional state embeddings and metastable clusters from time series data
http://arxiv.org/pdf/1906.00302v1

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
http://arxiv.org/pdf/1906.09360v1

Kernel Instrumental Variable Regression
http://arxiv.org/pdf/1906.00232v2

Symmetry-Based Disentangled Representation Learning requires Interaction with Environments
http://arxiv.org/pdf/1904.00243v3

Fast Efficient Hyperparameter Tuning for Policy Gradient Methods
http://arxiv.org/pdf/1902.06583v2

Offline Contextual Bayesian Optimization

Making the Cut: A Bandit-based Approach to Tiered Interviewing
http://arxiv.org/pdf/1906.09621v1

Unsupervised Scalable Representation Learning for Multivariate Time Series
http://arxiv.org/pdf/1901.10738v2

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?
http://arxiv.org/pdf/1906.02629v1

Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning
http://arxiv.org/pdf/1907.06290v1

Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks
http://arxiv.org/pdf/1909.07830v2

Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
http://arxiv.org/pdf/1905.12375v1

Optimal Sketching for Kronecker Product Regression and Low Rank Approximation

Distribution-Independent PAC Learning of Halfspaces with Massart Noise
http://arxiv.org/pdf/1906.10075v1

The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies
http://arxiv.org/pdf/1906.00425v2

Online Learning for Auxiliary Task Weighting for Reinforcement Learning

Blocking Bandits
http://arxiv.org/pdf/1907.11975v1

Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities
http://arxiv.org/pdf/1906.06776v2

Prior-Free Dynamic Auctions with Low Regret Buyers

On Single Source Robustness in Deep Fusion Models
http://arxiv.org/pdf/1906.04691v1

Policy Evaluation with Latent Confounders via Optimal Balance
http://arxiv.org/pdf/1908.01920v1

Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
http://arxiv.org/pdf/1905.03806v1

Adaptive Cross-Modal Few-shot Learning
http://arxiv.org/pdf/1902.07104v2

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
http://arxiv.org/pdf/1906.04948v1

Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees

One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
http://arxiv.org/pdf/1906.02773v1

Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces

Fair Algorithms for Clustering
http://arxiv.org/pdf/1901.02393v2

Learning Mean-Field Games

SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers
http://arxiv.org/pdf/1905.12107v1

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
http://arxiv.org/pdf/1905.02249v1

Deep Multivariate Quantiles for Novelty Detection

Fast Parallel Algorithms for Statistical Subset Selection Problems

PHYRE: A New Benchmark for Physical Reasoning
http://arxiv.org/pdf/1908.05656v1

How many variables should be entered in a principal component regression equation?
http://arxiv.org/pdf/1906.01139v1

Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery

Mutually Regressive Point Processes

Data-driven Estimation of Sinusoid Frequencies
http://arxiv.org/pdf/1906.00823v1

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
http://arxiv.org/pdf/1905.12202v1

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
http://arxiv.org/pdf/1904.09770v3

Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
http://arxiv.org/pdf/1907.00235v1

On the Accuracy of Influence Functions for Measuring Group Effects
http://arxiv.org/pdf/1905.13289v1

Face Reconstruction from Voice using Generative Adversarial Networks

Incremental Few-Shot Learning with Attention Attractor Networks
http://arxiv.org/pdf/1810.07218v2

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
http://arxiv.org/pdf/1906.08967v1

Input Similarity from the Neural Network Perspective

Adaptive Sequence Submodularity
http://arxiv.org/pdf/1902.05981v2

Weight Agnostic Neural Networks
http://arxiv.org/pdf/1906.04358v2

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction

Reducing the variance in online optimization by transporting past gradients
http://arxiv.org/pdf/1906.03532v2

Characterizing Bias in Classifiers using Generative Models
http://arxiv.org/pdf/1906.11891v1

Optimal Stochastic and Online Learning with Individual Iterates

Policy Learning for Fairness in Ranking
http://arxiv.org/pdf/1902.04056v2

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
http://arxiv.org/pdf/1807.07706v3

Markov Random Fields for Collaborative Filtering

A Step Toward Quantifying Independently Reproducible Machine Learning Research
http://arxiv.org/pdf/1909.06674v1

Scalable Global Optimization via Local Bayesian Optimization

Time-series Generative Adversarial Networks

On Accelerating Training of Transformer-Based Language Models
http://arxiv.org/pdf/1909.06695v1

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
http://arxiv.org/pdf/1908.04471v1

An Adaptive Empirical Bayesian Method for Sparse Deep Learning

Adaptive Influence Maximization with Myopic Feedback
http://arxiv.org/pdf/1905.11663v1

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
http://arxiv.org/pdf/1905.01707v4

Topology-Preserving Deep Image Segmentation
http://arxiv.org/pdf/1906.05404v1

Connective Cognition Network for Directional Visual Commonsense Reasoning

Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms
http://arxiv.org/pdf/1810.07301v2

A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning
http://arxiv.org/pdf/1902.00843v1

Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently

Learning Disentangled Representations for Recommendation

Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
http://arxiv.org/pdf/1905.13192v1

In-Place Near Zero-Cost Memory Protection for DNN

Acceleration via Symplectic Discretization of High-Resolution Differential Equations
http://arxiv.org/pdf/1902.03694v1

XLNet: Generalized Autoregressive Pretraining for Language Understanding
http://arxiv.org/pdf/1906.08237v1

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
http://arxiv.org/pdf/1907.11362v1

DM2C: Deep Mixed-Modal Clustering

MaCow: Masked Convolutional Generative Flow
http://arxiv.org/pdf/1902.04208v4

Learning by Abstraction: The Neural State Machine for Visual Reasoning

Adaptive Gradient-Based Meta-Learning Methods
http://arxiv.org/pdf/1906.02717v2

Equipping Experts/Bandits with Long-term Memory
http://arxiv.org/pdf/1905.12950v1

A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning
http://arxiv.org/pdf/1903.00725v2

Scalable inference of topic evolution via models for latent geometric structures
http://arxiv.org/pdf/1809.08738v2

Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network

Deep Active Learning with a Neural Architecture Search
http://arxiv.org/pdf/1811.07579v2

Efficiently escaping saddle points on manifolds
http://arxiv.org/pdf/1906.04321v2

AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
http://arxiv.org/pdf/1905.03381v1

DFNets: Spectral CNNs for Graphs with Feedback-looped Filters

Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning
http://arxiv.org/pdf/1905.11666v1

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
http://arxiv.org/pdf/1909.10818v1

Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
http://arxiv.org/pdf/1906.11366v1

Iterative Least Trimmed Squares for Mixed Linear Regression
http://arxiv.org/pdf/1902.03653v1

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
http://arxiv.org/pdf/1905.12784v1

Towards a Zero-One Law for Column Subset Selection

Compositional De-Attention Networks

Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning
http://arxiv.org/pdf/1907.05570v1

Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
http://arxiv.org/pdf/1811.04918v5

Mining GOLD Samples for Conditional GANs

Deep Model Transferability from Attribution Maps
http://arxiv.org/pdf/1909.11902v1

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
http://arxiv.org/pdf/1905.10271v1

Progressive Augmentation of GANs
http://arxiv.org/pdf/1901.10422v2

UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization

Meta-Surrogate Benchmarking for Hyperparameter Optimization
http://arxiv.org/pdf/1905.12982v1

Learning to Perform Local Rewriting for Combinatorial Optimization
http://arxiv.org/pdf/1810.00337v4

Anti-efficient encoding in emergent communication
http://arxiv.org/pdf/1905.12561v3

Singleshot : a scalable Tucker tensor decomposition

Neural Machine Translation with Soft Prototype

Reliable training and estimation of variance networks
http://arxiv.org/pdf/1906.03260v1

On the Statistical Properties of Multilabel Learning

Bayesian Learning of Sum-Product Networks
http://arxiv.org/pdf/1905.10884v1

Bayesian Batch Active Learning as Sparse Subset Approximation
http://arxiv.org/pdf/1908.02144v1

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
http://arxiv.org/pdf/1902.00792v2

The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks
http://arxiv.org/pdf/1906.02926v1

Single-Model Uncertainties for Deep Learning
http://arxiv.org/pdf/1811.00908v3

Is Deeper Better only when Shallow is Good?
http://arxiv.org/pdf/1903.03488v1

Wasserstein Weisfeiler-Lehman Graph Kernels
http://arxiv.org/pdf/1906.01277v1

Domain Generalization via Model-Agnostic Learning of Semantic Features

Grid Saliency for Context Explanations of Semantic Segmentation
http://arxiv.org/pdf/1907.13054v1

First-order methods almost always avoid saddle points: The case of Vanishing step-sizes
http://arxiv.org/pdf/1906.07772v1

Maximum Mean Discrepancy Gradient Flow
http://arxiv.org/pdf/1906.04370v1

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
http://arxiv.org/pdf/1907.02431v1

Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders
http://arxiv.org/pdf/1906.05423v1

Nonstochastic Multiarmed Bandits with Unrestricted Delays
http://arxiv.org/pdf/1906.00670v1

BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
http://arxiv.org/pdf/1902.02102v1

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
http://arxiv.org/pdf/1904.00687v2

Efficient Pure Exploration in Adaptive Round model

Multi-objects Generation with Amortized Structural Regularization
http://arxiv.org/pdf/1906.03923v1

Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time
http://arxiv.org/pdf/1907.07897v2

DetNAS: Backbone Search for Object Detection
http://arxiv.org/pdf/1903.10979v2

Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates
http://arxiv.org/pdf/1905.11768v1

Fast AutoAugment
http://arxiv.org/pdf/1905.00397v2

On the Convergence Rate of Training Recurrent Neural Networks in the Overparameterized Regime

Interval timing in deep reinforcement learning agents
http://arxiv.org/pdf/1905.13469v1

Graph-based Discriminators: Sample Complexity and Expressiveness
http://arxiv.org/pdf/1906.00264v1

Large Scale Structure of Neural Network Loss Landscapes
http://arxiv.org/pdf/1906.04724v1

Learning Nonsymmetric Determinantal Point Processes
http://arxiv.org/pdf/1905.12962v1

Hypothesis Set Stability and Generalization

Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
http://arxiv.org/pdf/1906.01140v2

Precision-Recall Balanced Topic Modelling

Learning Sparse Distributions using Iterative Hard Thresholding

Discriminative Topic Modeling with Logistic LDA
http://arxiv.org/pdf/1909.01436v1

Quantum Wasserstein Generative Adversarial Networks

Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion
http://arxiv.org/pdf/1906.00794v2

Hyperparameter Learning via Distributional Transfer
http://arxiv.org/pdf/1810.06305v3

Discriminator optimal transport

High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes

Are Anchor Points Really Indispensable in Label-Noise Learning?
http://arxiv.org/pdf/1906.00189v1

Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations

Differentiable Sorting using Optimal Transport: The Sinkhorn CDF and Quantile Operator
http://arxiv.org/pdf/1905.11885v1

Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
http://arxiv.org/pdf/1905.10259v2

Likelihood-Free Overcomplete ICA and ApplicationsIn Causal Discovery
http://arxiv.org/pdf/1909.01525v2

Interior-point Methods Strike Back: Solving the Wasserstein Barycenter Problem
http://arxiv.org/pdf/1905.12895v1

Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs

Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
http://arxiv.org/pdf/1905.10099v2

Efficient Non-Convex Stochastic Compositional Optimization Algorithm via Stochastic Recursive Gradient Descent

On the convergence of single-call stochastic extra-gradient methods
http://arxiv.org/pdf/1908.08465v1

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
http://arxiv.org/pdf/1901.10277v2

Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup
http://arxiv.org/pdf/1906.08632v1

GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs

Online Prediction of Switching Graph Labelings with Cluster Specialists
http://arxiv.org/pdf/1806.06439v3

Graph-Based Semi-Supervised Learning with Non-ignorable Non-response

BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
http://arxiv.org/pdf/1906.08158v1

A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off
http://arxiv.org/pdf/1906.00771v1

Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs
http://arxiv.org/pdf/1902.07605v1

Cross-lingual Language Model Pretraining
http://arxiv.org/pdf/1901.07291v1

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
http://arxiv.org/pdf/1905.13633v1

Universal Invariant and Equivariant Graph Neural Networks
http://arxiv.org/pdf/1905.04943v1

The bias of the sample mean in multi-armed bandits can be positive or negative
http://arxiv.org/pdf/1905.11397v1

On the Correctness and Sample Complexity of Inverse Reinforcement Learning
http://arxiv.org/pdf/1906.00422v1

VIREL: A Variational Inference Framework for Reinforcement Learning
http://arxiv.org/pdf/1811.01132v6

First Order Motion Model for Image Animation

Tensor Monte Carlo: Particle Methods for the GPU era
http://arxiv.org/pdf/1806.08593v3

Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction
http://arxiv.org/pdf/1906.01401v3

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
http://arxiv.org/pdf/1904.10090v2

Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning

Toward a Characterization of Loss Functions for Distribution Learning
http://arxiv.org/pdf/1906.02652v2

Coresets for Archetypal Analysis

Emergence of Object Segmentation in Perturbed Generative Models
http://arxiv.org/pdf/1905.12663v1

Optimal Sparse Decision Trees
http://arxiv.org/pdf/1904.12847v1

Escaping from saddle points on Riemannian manifolds
http://arxiv.org/pdf/1906.07355v1

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
http://arxiv.org/pdf/1906.02687v2

Modeling Tabular data using Conditional GAN
http://arxiv.org/pdf/1907.00503v1

Normalization Helps Training of Quantized LSTM

Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration
http://arxiv.org/pdf/1906.10114v1

Deep Scale-spaces: Equivariance Over Scale
http://arxiv.org/pdf/1905.11697v1

GRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series
http://arxiv.org/pdf/1905.12374v1

Estimating Convergence of Markov chains with L-Lag Couplings

Learning-Based Low-Rank Approximations

Implicit Regularization in Deep Matrix Factorization
http://arxiv.org/pdf/1905.13655v2

List-decodable Linear Regression
http://arxiv.org/pdf/1905.05679v3

Learning elementary structures for 3D shape generation and matching
http://arxiv.org/pdf/1908.04725v2

On the Hardness of Robust Classification

Foundations of Comparison-Based Hierarchical Clustering
http://arxiv.org/pdf/1811.00928v2

What the Vec? Towards Probabilistically Grounded Embeddings
http://arxiv.org/pdf/1805.12164v2

Minimizers of the Empirical Risk and Risk Monotonicity

Explicit Planning for Efficient Exploration in Reinforcement Learning

Lower Bounds on Adversarial Robustness from Optimal Transport
http://arxiv.org/pdf/1909.12272v1

Neural Spline Flows
http://arxiv.org/pdf/1906.04032v1

Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints
http://arxiv.org/pdf/1905.10825v3

Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization
http://arxiv.org/pdf/1906.02107v1

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
http://arxiv.org/pdf/1906.00957v1

An adaptive nearest neighbor rule for classification
http://arxiv.org/pdf/1905.12717v1

Coresets for Clustering with Fairness Constraints
http://arxiv.org/pdf/1906.08484v3

PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments

MAVEN: Multi-Agent Variational Exploration

Competitive Gradient Descent
http://arxiv.org/pdf/1905.12103v1

Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
http://arxiv.org/pdf/1907.01771v1

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
http://arxiv.org/pdf/1907.02114v1

Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes

Sequence Modelling with Unconstrained Generation Order

Probabilistic Logic Neural Networks for Reasoning
http://arxiv.org/pdf/1906.08495v1

A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families
http://arxiv.org/pdf/1907.08306v1

A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening
http://arxiv.org/pdf/1902.09702v4

Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond
http://arxiv.org/pdf/1906.07868v1

The Implicit Bias of AdaGrad on Separable Data
http://arxiv.org/pdf/1906.03559v1

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?
http://arxiv.org/pdf/1905.09803v1

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks
http://arxiv.org/pdf/1908.06378v1

Re-examination of the Role of Latent Variables in Sequence Modeling
http://arxiv.org/pdf/1902.01388v2

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
http://arxiv.org/pdf/1903.02380v1

A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment
http://arxiv.org/pdf/1907.12392v3

SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies

Towards Understanding the Importance of Shortcut Connections in Residual Networks
http://arxiv.org/pdf/1909.04653v2

Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
http://arxiv.org/pdf/1906.00097v1

Solving Interpretable Kernel Dimensionality Reduction
http://arxiv.org/pdf/1909.03093v3

Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space

A Model to Search for Synthesizable Molecules
http://arxiv.org/pdf/1906.05221v1

Post training 4-bit quantization of convolutional networks for rapid-deployment
http://arxiv.org/pdf/1810.05723v3

Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes
http://arxiv.org/pdf/1906.07697v1

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
http://arxiv.org/pdf/1905.12849v1

Solving graph compression via optimal transport
http://arxiv.org/pdf/1905.12158v1

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Stability of Graph Scattering Transforms
http://arxiv.org/pdf/1906.04784v1

A Debiased MDI Feature Importance Measure for Random Forests
http://arxiv.org/pdf/1906.10845v1

Difference Maximization Q-learning: Provably Efficient Q-learning with Function Approximation

Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
http://arxiv.org/pdf/1810.11905v3

Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks
http://arxiv.org/pdf/1905.10961v1

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
http://arxiv.org/pdf/1904.11955v1

Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning
http://arxiv.org/pdf/1909.10549v1

Chirality Nets for Human Pose Regression

Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds
http://arxiv.org/pdf/1908.01842v1

Fast Decomposable Submodular Function Minimization using Constrained Total Variation
http://arxiv.org/pdf/1905.11327v1

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
http://arxiv.org/pdf/1907.04164v1

Spherical Text Embedding

Möbius Transformation for Fast Inner Product Search on Graph

Hyperbolic Graph Neural Networks

Average Individual Fairness: Algorithms, Generalization and Experiments
http://arxiv.org/pdf/1905.10607v1

Fixing the train-test resolution discrepancy
http://arxiv.org/pdf/1906.06423v2

Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes
http://arxiv.org/pdf/1905.10413v1

Manipulating a Learning Defender and Ways to Counteract
http://arxiv.org/pdf/1905.11759v1

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
http://arxiv.org/pdf/1906.04705v1

Certifiable Robustness to Graph Perturbations

Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay
http://arxiv.org/pdf/1905.09992v1

Paradoxes in Fair Machine Learning

Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost
http://arxiv.org/pdf/1907.06246v1

The spiked matrix model with generative priors
http://arxiv.org/pdf/1905.12385v2

Gradient Dynamics of Shallow Low-Dimensional ReLU Networks

Robust and Communication-Efficient Collaborative Learning
http://arxiv.org/pdf/1907.10595v1

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
http://arxiv.org/pdf/1906.06781v2

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
http://arxiv.org/pdf/1905.12842v1

A Universally Optimal Multistage Accelerated Stochastic Gradient Method
http://arxiv.org/pdf/1901.08022v2

From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction

Large Memory Layers with Product Keys
http://arxiv.org/pdf/1907.05242v1

Learning Deterministic Weighted Automata with Queries and Counterexamples

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
http://arxiv.org/pdf/1902.06720v3

Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals

Visualizing and Measuring the Geometry of BERT
http://arxiv.org/pdf/1906.02715v1

Self-Critical Reasoning for Robust Visual Question Answering
http://arxiv.org/pdf/1905.09998v1

Learning to Screen

A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers

A Little Is Enough: Circumventing Defenses For Distributed Learning
http://arxiv.org/pdf/1902.06156v1

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
http://arxiv.org/pdf/1907.08226v2

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
http://arxiv.org/pdf/1909.06841v1

Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network
http://arxiv.org/pdf/1905.13290v2

The Functional Neural Process

Recurrent Registration Neural Networks for Deformable Image Registration
http://arxiv.org/pdf/1906.09988v1

Unsupervised State Representation Learning in Atari
http://arxiv.org/pdf/1906.08226v2

Unlocking Fairness: a Trade-off Revisited

Fisher Efficient Inference of Intractable Models

Thompson Sampling and Approximate Inference
http://arxiv.org/pdf/1908.04970v1

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
http://arxiv.org/pdf/1902.05454v2

Online Learning via the Differential Privacy Lens
http://arxiv.org/pdf/1711.10019v3

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
http://arxiv.org/pdf/1806.10574v3

Adaptively Aligned Image Captioning via Adaptive Attention Time
http://arxiv.org/pdf/1909.09060v1

Accurate Uncertainty Estimation and Decomposition in Ensemble Learning

Learning Bayesian Networks with Low Rank Conditional Probability Tables
http://arxiv.org/pdf/1905.12552v1

Equal Opportunity in Online Classification with Partial Feedback
http://arxiv.org/pdf/1902.02242v1

Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations

Neural Multisensory Scene Inference

Regret Bounds for Thompson Sampling in Restless Bandit Problems
http://arxiv.org/pdf/1905.12673v1

What Can ResNet Learn Efficiently, Going Beyond Kernels?
http://arxiv.org/pdf/1905.10337v2

Better Transfer Learning Through Inferred Successor Maps

Unsupervised Co-Learning on 

G

-Manifolds Across Irreducible Representations

Defending Against Neural Fake News
http://arxiv.org/pdf/1905.12616v1

Sample Adaptive MCMC

A Stochastic Composite Gradient Method with Incremental Variance Reduction
http://arxiv.org/pdf/1906.10186v1

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
http://arxiv.org/pdf/1906.08899v1

Reconciling meta-learning and continual learning with online mixtures of tasks
http://arxiv.org/pdf/1812.06080v3

Distributionally Robust Optimization and Generalization in Kernel Methods

A General Theory of Equivariant CNNs on Homogeneous Spaces
http://arxiv.org/pdf/1811.02017v1

Trivializations for Gradient-Based Optimization on Manifolds
http://arxiv.org/pdf/1909.09501v1

Write, Execute, Assess: Program Synthesis with a REPL
http://arxiv.org/pdf/1906.04604v1

A Meta-Analysis of Overfitting in Machine Learning

(Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs
http://arxiv.org/pdf/1805.02349v2

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
http://arxiv.org/pdf/1905.12564v1

GNNExplainer: Generating Explanations for Graph Neural Networks
http://arxiv.org/pdf/1903.03894v2

Linear Stochastic Bandits Under Safety Constraints
http://arxiv.org/pdf/1908.05814v1

A coupled autoencoder approach for multi-modal analysis of cell types

Towards Automatic Concept-based Explanations
http://arxiv.org/pdf/1902.03129v2

A Deep Probabilistic Model for Compressing Low Resolution Videos

Budgeted Reinforcement Learning in Continuous State Space
http://arxiv.org/pdf/1903.01004v3

The Discovery of Useful Questions as Auxiliary Tasks
http://arxiv.org/pdf/1909.04607v1

Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm
http://arxiv.org/pdf/1905.13194v1

Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias
http://arxiv.org/pdf/1906.06766v1

Correlation clustering with local objectives

Multiclass Performance Metric Elicitation

Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
http://arxiv.org/pdf/1907.07502v1

Explicit Explore-Exploit Algorithms in Continuous State Spaces

ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls
http://arxiv.org/pdf/1905.11465v2

Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices
http://arxiv.org/pdf/1909.06349v1

Understanding Posterior Collapse in Variational Autoencoders

Language as an Abstraction for Hierarchical Deep Reinforcement Learning
http://arxiv.org/pdf/1906.07343v1

Efficient online learning with kernels for adversarial large scale problems
http://arxiv.org/pdf/1902.09917v2

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
http://arxiv.org/pdf/1809.03113v3

Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels

Non-Cooperative Inverse Reinforcement Learning

DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization
http://arxiv.org/pdf/1901.05134v1

Sobolev Independence Criterion

Maximum Entropy Monte-Carlo Planning

Learning from brains how to regularize machines

Using Statistics to Automate Stochastic Optimization
http://arxiv.org/pdf/1909.09785v1

Zero-shot Knowledge Transfer via Adversarial Belief Matching
http://arxiv.org/pdf/1905.09768v3

Differentiable Convex Optimization Layers

Random Tessellation Forests
http://arxiv.org/pdf/1906.05440v1

Learning Nearest Neighbor Graphs from Noisy Distance Samples
http://arxiv.org/pdf/1905.13267v1

Lookahead Optimizer: k steps forward, 1 step back
http://arxiv.org/pdf/1907.08610v1

Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
http://arxiv.org/pdf/1908.01210v1

Covariate-Powered Empirical Bayes Estimation
http://arxiv.org/pdf/1906.01611v1

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
http://arxiv.org/pdf/1904.00956v1

Marginalized Off-Policy Evaluation for Reinforcement Learning

Contextual Bandits with Cross-Learning
http://arxiv.org/pdf/1809.09582v1

Evaluating Protein Transfer Learning with TAPE
http://arxiv.org/pdf/1906.08230v1

A Bayesian Theory of Conformity in Collective Decision Making

Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
http://arxiv.org/pdf/1810.05369v3

Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation
http://arxiv.org/pdf/1905.03684v2

A Benchmark for Interpretability Methods in Deep Neural Networks

Memory Efficient Adaptive Optimization
http://arxiv.org/pdf/1901.11150v2

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
http://arxiv.org/pdf/1905.10395v1

A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks
http://arxiv.org/pdf/1902.08722v3

Neural Jump Stochastic Differential Equations
http://arxiv.org/pdf/1905.10403v1

Learning metrics for persistence-based summaries and applications for graph classification
http://arxiv.org/pdf/1904.12189v1

ON THE VALUE OF TARGET SAMPLING IN COVARIATE-SHIFT

Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization
http://arxiv.org/pdf/1907.09150v1

On Robustness of Principal Component Regression

Meta Learning with Relational Information for Short Sequences
http://arxiv.org/pdf/1909.02105v1

Residual Flows for Invertible Generative Modeling

Multi-Agent Common Knowledge Reinforcement Learning
http://arxiv.org/pdf/1810.11702v4

Learning to Learn By Self-Critique
http://arxiv.org/pdf/1905.10295v4

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
http://arxiv.org/pdf/1901.01570v1

Dying Experts: Efficient Algorithms with Optimal Regret Bounds

Model similarity mitigates test set overuse
http://arxiv.org/pdf/1905.12580v1

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
http://arxiv.org/pdf/1909.03194v1

Hierarchical Decision Making by Generating and Following Natural Language Instructions
http://arxiv.org/pdf/1906.00744v4

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
http://arxiv.org/pdf/1905.13360v1

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
http://arxiv.org/pdf/1902.07826v2

Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models

Logarithmic Regret for Online Control
http://arxiv.org/pdf/1909.05062v1

Elliptical Perturbations for Differential Privacy
http://arxiv.org/pdf/1905.09420v1

Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
http://arxiv.org/pdf/1909.03496v1

KNG: The K-Norm Gradient Mechanism
http://arxiv.org/pdf/1905.09436v1

CXPlain: Causal Explanations for Model Interpretation under Uncertainty

Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning
http://arxiv.org/pdf/1909.03245v1

STREETS: A Novel Camera Network Dataset for Traffic Flow

Sequential Neural Processes
http://arxiv.org/pdf/1906.10264v3

Policy Continuation with Hindsight Inverse Dynamics

Learning to Self-Train for Semi-Supervised Few-Shot Classification
http://arxiv.org/pdf/1906.00562v1

Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.
http://arxiv.org/pdf/1909.06628v1

From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization
http://arxiv.org/pdf/1903.04268v3

On the Expressive Power of Deep Polynomial Neural Networks
http://arxiv.org/pdf/1905.12207v1

DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation
http://arxiv.org/pdf/1907.12205v1

Can SGD Learn Recurrent Neural Networks with Provable Generalization?
http://arxiv.org/pdf/1902.01028v2

Limits of Private Learning with Access to Public Data

Discrete Object Generation with Reversible Inductive Construction
http://arxiv.org/pdf/1907.08268v1

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
http://arxiv.org/pdf/1907.12189v1

Functional Adversarial Attacks
http://arxiv.org/pdf/1906.00001v1

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
http://arxiv.org/pdf/1905.12412v2

Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin
http://arxiv.org/pdf/1908.11335v1

Same-Cluster Querying for Overlapping Clusters

Efficient Convex Relaxations for Streaming PCA

Learning Robust Global Representations by Penalizing Local Predictive Power
http://arxiv.org/pdf/1905.13549v1

Unsupervised Curricula for Visual Meta-Reinforcement Learning

Sample Complexity of Learning Mixture of Sparse Linear Regressions

Large Scale Adversarial Representation Learning
http://arxiv.org/pdf/1907.02544v1

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
http://arxiv.org/pdf/1907.00208v1

Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples
http://arxiv.org/pdf/1909.11907v1

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
http://arxiv.org/pdf/1906.08399v1

Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence
http://arxiv.org/pdf/1905.13277v1

Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering

Variational Graph Recurrent Neural Networks
http://arxiv.org/pdf/1908.09710v2

Semi-Implicit Graph Variational Auto-Encoders
http://arxiv.org/pdf/1908.07078v3

Unsupervised Learning of Object Keypoints for Perception and Control
http://arxiv.org/pdf/1906.11883v1

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
http://arxiv.org/pdf/1907.07330v1

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
http://arxiv.org/pdf/1906.10816v3

Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks
http://arxiv.org/pdf/1905.13210v2

High-Dimensional Optimization in Adaptive Random Subspaces
http://arxiv.org/pdf/1906.11809v2

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
http://arxiv.org/pdf/1909.02330v2

How to Initialize your Network? Robust Initialization for WeightNorm & ResNets
http://arxiv.org/pdf/1906.02341v1

On Making Stochastic Classifiers Deterministic

Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection
http://arxiv.org/pdf/1907.03813v1

Improving Black-box Adversarial Attacks with a Transfer-based Prior
http://arxiv.org/pdf/1906.06919v1

Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
http://arxiv.org/pdf/1906.02174v3

Statistical Model Aggregation via Parameter Matching

On the (in)fidelity and sensitivity of explanations
http://arxiv.org/pdf/1901.09392v2

Exponential Family Estimation via Adversarial Dynamics Embedding
http://arxiv.org/pdf/1904.12083v1

The Broad Optimality of Profile Maximum Likelihood
http://arxiv.org/pdf/1906.03794v3

MintNet: Building Invertible Neural Networks with Masked Convolutions
http://arxiv.org/pdf/1907.07945v1

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
http://arxiv.org/pdf/1905.12926v1

MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
http://arxiv.org/pdf/1906.00547v1

Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
http://arxiv.org/pdf/1906.09531v1

Online Forecasting of Total-Variation-bounded Sequences
http://arxiv.org/pdf/1906.03364v1

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
http://arxiv.org/pdf/1906.05963v1

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
http://arxiv.org/pdf/1905.11006v1

Unlabeled Data Improves Adversarial Robustness
http://arxiv.org/pdf/1905.13736v2

Machine Teaching of Active Sequential Learners

Gaussian-Based Pooling for Convolutional Neural Networks

Meta Architecture Search

NAOMI: Non-Autoregressive Multiresolution Sequence Imputation
http://arxiv.org/pdf/1901.10946v2

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
http://arxiv.org/pdf/1906.00569v1

Private Stochastic Convex Optimization with Optimal Rates
http://arxiv.org/pdf/1908.09970v1

Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
http://arxiv.org/pdf/1906.04584v3

Demystifying Black-box Models with Symbolic Metamodels

Neural Temporal-Difference Learning Converges to Global Optima
http://arxiv.org/pdf/1905.10027v1

Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces
http://arxiv.org/pdf/1901.10634v2

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
http://arxiv.org/pdf/1906.00618v1

Faster Boosting with Smaller Memory
http://arxiv.org/pdf/1901.09047v2

Variance Reduction for Matrix Games
http://arxiv.org/pdf/1907.02056v1

Learning Neural Networks with Adaptive Regularization

Distributed estimation of the inverse Hessian by determinantal averaging
http://arxiv.org/pdf/1905.11546v1

Smoothing Structured Decomposable Circuits
http://arxiv.org/pdf/1906.00311v1

Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
http://arxiv.org/pdf/1906.04893v1

Provable Non-linear Inductive Matrix Completion

Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback
http://arxiv.org/pdf/1905.10936v1

Sparse Variational Inference: Bayesian Coresets from Scratch
http://arxiv.org/pdf/1906.03329v1

Many-Armed Bandits with High-Dimensional Contexts under a Low-Rank Structure

A Necessary and Sufficient Stability Notion for Adaptive Generalization
http://arxiv.org/pdf/1906.00930v1

Necessary and Sufficient Geometries for Adaptive Gradient Algorithms

Landmark Ordinal Embedding

Identification of Conditional Causal Effects under Markov Equivalence

The Thermodynamic Variational Objective
http://arxiv.org/pdf/1907.00031v1

Global Guarantees for Blind Demodulation with Generative Priors
http://arxiv.org/pdf/1905.12576v1

Exact sampling of determinantal point processes with sublinear time preprocessing
http://arxiv.org/pdf/1905.13476v2

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
http://arxiv.org/pdf/1906.00729v1

Uniform convergence may be unable to explain generalization in deep learning
http://arxiv.org/pdf/1902.04742v2

A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
http://arxiv.org/pdf/1709.07536v4

DTWNet: a Dynamic Time Warping Network

Structured Graph Learning Via Laplacian Spectral Constraints
http://arxiv.org/pdf/1909.11594v1

Thresholding Bandit with Optimal Aggregate Regret
http://arxiv.org/pdf/1905.11046v1

Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks
http://arxiv.org/pdf/1907.04595v1

Rethinking Kernel Methods for Node Representation Learning on Graphs

Causal Misidentification in Imitation Learning

Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection
http://arxiv.org/pdf/1905.10881v1

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
http://arxiv.org/pdf/1909.09314v1

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
http://arxiv.org/pdf/1906.00949v1

Bayesian Optimization with Unknown Search Space

On the Downstream Performance of Compressed Word Embeddings
http://arxiv.org/pdf/1909.01264v1

Multivariate Distributionally Robust Convex Regression under Absolute Error Loss
http://arxiv.org/pdf/1905.12231v1

Neural Relational Inference with Fast Modular Meta-learning

Gradient based sample selection for online continual learning
http://arxiv.org/pdf/1903.08671v4

Attribution-Based Confidence Metric For Deep Neural Networks

Theoretical evidence for adversarial robustness through randomization
http://arxiv.org/pdf/1902.01148v2

Online Continual Learning with Maximal Interfered Retrieval
http://arxiv.org/pdf/1908.04742v2

Neural Attribution for Semantic Bug-Localization in Student Programs

Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
http://arxiv.org/pdf/1906.07987v1

SPoC: Search-based Pseudocode to Code
http://arxiv.org/pdf/1906.04908v1

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
http://arxiv.org/pdf/1909.12078v1

Margin-Based Generalization Lower Bounds for Boosted Classifiers

Connections Between Mirror Descent, Thompson Sampling and the Information Ratio
http://arxiv.org/pdf/1905.11817v1

Graph Transformer Networks

Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder
http://arxiv.org/pdf/1905.09027v1

The Impact of Regularization on High-dimensional Logistic Regression
http://arxiv.org/pdf/1906.03761v3

Adaptive Density Estimation for Generative Models

Fast and Provable ADMM for Learning with Generative Priors
http://arxiv.org/pdf/1907.03343v1

Weighted Linear Bandits for Non-Stationary Environments
http://arxiv.org/pdf/1909.09146v1

Improved Regret Bounds for Bandit Combinatorial Optimization

Pareto Multi-Task Learning

SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits
http://arxiv.org/pdf/1809.08151v3

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
http://arxiv.org/pdf/1906.05247v2

Integer Discrete Flows and Lossless Compression
http://arxiv.org/pdf/1905.07376v2

Structured Prediction with Projection Oracles

Primal Dual Formulation For Deep Learning With Constraints

Screening Sinkhorn Algorithm for Regularized Optimal Transport
http://arxiv.org/pdf/1906.08540v1

PAC-Bayes Un-Expected Bernstein Inequality
http://arxiv.org/pdf/1905.13367v1

Are Labels Required for Improving Adversarial Robustness?
http://arxiv.org/pdf/1905.13725v3

Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
http://arxiv.org/pdf/1905.11527v1

Multi-objective Bayesian optimisation with preferences over objectives
http://arxiv.org/pdf/1902.04228v1

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
http://arxiv.org/pdf/1905.10887v1

Theoretical Analysis Of Adversarial Learning: A Minimax Approach
http://arxiv.org/pdf/1811.05232v2

Multiagent Evaluation under Incomplete Information
http://arxiv.org/pdf/1909.09849v1

Tree-Sliced Variants of Wasserstein Distances
http://arxiv.org/pdf/1902.00342v2

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
http://arxiv.org/pdf/1906.03849v2

Towards Interpretable Reinforcement Learning Using Attention Augmented Agents
http://arxiv.org/pdf/1906.02500v1

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
http://arxiv.org/pdf/1904.01750v1

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
http://arxiv.org/pdf/1906.03161v1

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
http://arxiv.org/pdf/1906.08253v1

Correlation Clustering with Adaptive Similarity Queries
http://arxiv.org/pdf/1905.11902v1

Control What You Can: Intrinsically Motivated Task-Planning Agent
http://arxiv.org/pdf/1906.08190v1

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
http://arxiv.org/pdf/1906.01164v1

Beating SGD Saturation with Tail-Averaging and Minibatching
http://arxiv.org/pdf/1902.08668v2

Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond

Continuous-time Models for Stochastic Optimization Algorithms
http://arxiv.org/pdf/1810.02565v2

Curriculum-guided Hindsight Experience Replay

Implicit Semantic Data Augmentation for Deep Networks
http://arxiv.org/pdf/1909.12220v1

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
http://arxiv.org/pdf/1907.01543v1

Shadowing Properties of Optimization Algorithms

Causal Regularization
http://arxiv.org/pdf/1702.02604v2

Learning Hawkes Processes from a handful of events

Unsupervised Object Segmentation by Redrawing
http://arxiv.org/pdf/1905.13539v1

Regret Bounds for Learning State Representations in Reinforcement Learning

Band-Limited Gaussian Processes: The Sinc Kernel
http://arxiv.org/pdf/1909.07279v1

Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification
http://arxiv.org/pdf/1906.05082v1

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
http://arxiv.org/pdf/1906.01732v2

Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
http://arxiv.org/pdf/1909.06161v1

k-Means Clustering of Lines for Big Data

Random projections and sampling algorithms for clustering of high-dimensional polygonal curves
http://arxiv.org/pdf/1907.06969v1

Recurrent Space-time Graph Neural Networks
http://arxiv.org/pdf/1904.05582v3

Uncertainty on Asynchronous Event Prediction

Accurate, reliable and fast robustness evaluation
http://arxiv.org/pdf/1907.01003v1

Sparse High-Dimensional Isotonic Regression
http://arxiv.org/pdf/1907.01715v1

Triad Constraints for Learning Causal Structure of Latent Variables

On the Inductive Bias of Neural Tangent Kernels
http://arxiv.org/pdf/1905.12173v1

Cross-Domain Transferable Perturbations

Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices

Kernel quadrature with DPPs
http://arxiv.org/pdf/1906.07832v1

REM: From Structural Entropy to Community Structure Deception

Sim2real transfer learning for 3D pose estimation: motion to the rescue
http://arxiv.org/pdf/1907.02499v1

Self-Supervised Deep Learning on Point Clouds by Reconstructing Space
http://arxiv.org/pdf/1901.08396v2

Piecewise Strong Convexity of Neural Networks
http://arxiv.org/pdf/1810.12805v2

Minimum Stein Discrepancy Estimators
http://arxiv.org/pdf/1906.08283v1

Fast and Furious Learning in Zero-Sum Games: Vanishing Regret with Non-Vanishing Step Sizes
http://arxiv.org/pdf/1905.04532v1

Generalization Bounds for Neural Networks via Approximate Description Length

Provably robust boosted decision stumps and trees against adversarial attacks
http://arxiv.org/pdf/1906.03526v1

Convergence of Adversarial Training in Overparametrized Neural Networks
http://arxiv.org/pdf/1906.07916v1

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
http://arxiv.org/pdf/1905.03197v1

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
http://arxiv.org/pdf/1905.11071v1

Biases for Emergent Communication in Multi-agent Reinforcement Learning

Episodic Memory in Lifelong Language Learning
http://arxiv.org/pdf/1906.01076v1

A Simple Baseline for Bayesian Uncertainty in Deep Learning
http://arxiv.org/pdf/1902.02476v1

Communication-efficient Distributed SGD with Sketching
http://arxiv.org/pdf/1903.04488v2

Modeling Conceptual Understanding in Image Reference Games

Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights
http://arxiv.org/pdf/1905.11436v1

Near Neighbor: Who is the Fairest of Them All?
http://arxiv.org/pdf/1906.02640v1

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
http://arxiv.org/pdf/1902.10652v3

Assessing Social and Intersectional Biases in Contextualized Word Representations

Online Convex Matrix Factorization with Representative Regions
http://arxiv.org/pdf/1904.02580v2

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
http://arxiv.org/pdf/1906.08988v1

The continuous Bernoulli: fixing a pervasive error in variational autoencoders
http://arxiv.org/pdf/1907.06845v2

Privacy Amplification by Mixing and Diffusion Mechanisms
http://arxiv.org/pdf/1905.12264v1

Variance Reduction in Bipartite Experiments through Correlation Clustering

Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
http://arxiv.org/pdf/1906.04585v1

Metalearned Neural Memory
http://arxiv.org/pdf/1907.09720v1

Learning Multiple Markov Chains via Adaptive Allocation
http://arxiv.org/pdf/1905.11128v1

Diffusion Improves Graph Learning

Deep Random Splines for Point Process Intensity Estimation of Neural Population Data
http://arxiv.org/pdf/1903.02610v2

Variational Bayes under Model Misspecification
http://arxiv.org/pdf/1905.10859v1

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
http://arxiv.org/pdf/1905.10994v1

Optimal Sampling and Clustering in the Stochastic Block Model

Recurrent Kernel Networks
http://arxiv.org/pdf/1906.03200v1

Cold Case: The Lost MNIST Digits
http://arxiv.org/pdf/1905.10498v1

Hierarchical Optimal Transport for Multimodal Distribution Alignment
http://arxiv.org/pdf/1906.11768v1

Exploration via Hindsight Goal Generation
http://arxiv.org/pdf/1906.04279v1

Shaping Belief States with Generative Environment Models for RL
http://arxiv.org/pdf/1906.09237v2

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
http://arxiv.org/pdf/1812.09603v1

Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions
http://arxiv.org/pdf/1902.08825v2

RUDDER: Return Decomposition for Delayed Rewards
http://arxiv.org/pdf/1806.07857v3

Graph Normalizing Flows
http://arxiv.org/pdf/1905.13177v1

Explanations can be manipulated and geometry is to blame
http://arxiv.org/pdf/1906.07983v2

Communication trade-offs for synchronized distributed SGD with large step size
http://arxiv.org/pdf/1904.11325v1

Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
http://arxiv.org/pdf/1905.12080v1

No-Regret Learning in Unknown Games with Correlated Payoffs
http://arxiv.org/pdf/1909.08540v1

Alleviating Label Switching with Optimal Transport

Paraphrase Generation with Latent Bag of Words

An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
http://arxiv.org/pdf/1807.00736v1

Compacting, Picking and Growing for Unforgetting Continual Learning

Approximating Interactive Human Evaluation withSelf-Play for Open-Domain Dialog Systems
http://arxiv.org/pdf/1906.09308v1

A New Distribution on the Simplex with Auto-Encoding Applications
http://arxiv.org/pdf/1905.12052v2

AutoPrun: Automatic Network Pruning by Regularizing Auxiliary Parameters

A neurally plausible model learns successor representations in partially observable environments
http://arxiv.org/pdf/1906.09480v1

Learning about an exponential amount of conditional distributions
http://arxiv.org/pdf/1902.08401v1

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
http://arxiv.org/pdf/1805.08349v1

Using Embeddings to Correct for Unobserved Confounding in Networks
http://arxiv.org/pdf/1902.04114v2

PolyTree framework for tree ensemble analysis

Bayesian Optimization under Heavy-tailed Payoffs
http://arxiv.org/pdf/1909.07040v1

Combining Generative and Discriminative Models for Hybrid Inference
http://arxiv.org/pdf/1906.02547v3

A Graph Theoretic Additive Approximation of Optimal Transport
http://arxiv.org/pdf/1905.11830v2

Adversarial Robustness through Local Linearization
http://arxiv.org/pdf/1907.02610v1

Sampled softmax with random Fourier features
http://arxiv.org/pdf/1907.10747v1

Semi-flat minima and saddle points by embedding neural networks to overparameterization
http://arxiv.org/pdf/1906.04868v2

Learning Fairness in Multi-Agent Systems

Primal-Dual Block Frank-Wolfe
http://arxiv.org/pdf/1906.02436v1

GOT: An Optimal Transport framework for Graph comparison
http://arxiv.org/pdf/1906.02085v1

On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
http://arxiv.org/pdf/1905.11001v2

Complexity of Highly Parallel Non-Smooth Convex Optimization
http://arxiv.org/pdf/1906.10655v1

Inverting Deep Generative models, One layer at a time

Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization

The Implicit Metropolis-Hastings Algorithm
http://arxiv.org/pdf/1906.03644v1

An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints
http://arxiv.org/pdf/1906.11357v1

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck

Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift
http://arxiv.org/pdf/1906.02530v1

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
http://arxiv.org/pdf/1905.09963v2

Defending Neural Backdoors via Generative Distribution Modeling

Are Sixteen Heads Really Better than One?
http://arxiv.org/pdf/1905.10650v2

Multi-resolution Multi-task Gaussian Processes
http://arxiv.org/pdf/1906.08344v1

Variational Bayesian Optimal Experimental Design
http://arxiv.org/pdf/1903.05480v2

Universal Approximation of Input-Output Maps by Temporal Convolutional Nets
http://arxiv.org/pdf/1906.09211v1

Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes
http://arxiv.org/pdf/1903.08778v2

Reinforcement Learning with Convex Constraints
http://arxiv.org/pdf/1906.09323v1

User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning

Stochastic Bandits with Context Distributions
http://arxiv.org/pdf/1906.02685v1

Inducing brain-relevant bias in natural language processing models

Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
http://arxiv.org/pdf/1906.00572v1

Recovering Bandits

Computing Linear Restrictions of Neural Networks
http://arxiv.org/pdf/1908.06214v1

Learning Positive Functions with Pseudo Mirror Descent

Correlation Priors for Reinforcement Learning
http://arxiv.org/pdf/1909.05106v1

Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression
http://arxiv.org/pdf/1907.07167v1

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?
http://arxiv.org/pdf/1905.12506v1

PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
http://arxiv.org/pdf/1905.13727v2

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
http://arxiv.org/pdf/1905.09944v1

Sample Efficient Active Learning of Causal Trees

Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection
http://arxiv.org/pdf/1909.02293v1

Robust Attribution Regularization
http://arxiv.org/pdf/1905.09957v1

Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization

When to use parametric models in reinforcement learning?
http://arxiv.org/pdf/1906.05243v1

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
http://arxiv.org/pdf/1905.11488v1

Categorized Bandits

Worst-Case Regret Bounds for Exploration via Randomized Value Functions
http://arxiv.org/pdf/1906.02870v3

Efficient characterization of electrically evoked responses for neural interfaces

Differentially Private Distributed Data Summarization under Covariate Shift

Hamiltonian descent for composite objectives
http://arxiv.org/pdf/1906.02608v1

Implicit Regularization of Accelerated Methods in Hilbert Spaces
http://arxiv.org/pdf/1905.13000v3

Non-Asymptotic Pure Exploration by Solving Games
http://arxiv.org/pdf/1906.10431v1

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
http://arxiv.org/pdf/1909.01504v1

Cormorant: Covariant Molecular Neural Networks
http://arxiv.org/pdf/1906.04015v1

Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
http://arxiv.org/pdf/1905.13472v1

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
http://arxiv.org/pdf/1902.03475v1

Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets
http://arxiv.org/pdf/1906.06247v1

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
http://arxiv.org/pdf/1903.08114v1

Bayesian Layers: A Module for Neural Network Uncertainty
http://arxiv.org/pdf/1812.03973v3

Learning Compositional Neural Programs with Recursive Tree Search and Planning
http://arxiv.org/pdf/1905.12941v1

Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric

Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations
http://arxiv.org/pdf/1906.02367v1

Likelihood Ratios for Out-of-Distribution Detection
http://arxiv.org/pdf/1906.02845v1

Discrete Flows: Invertible Generative Models of Discrete Data
http://arxiv.org/pdf/1905.10347v1

Mindreader: A Self Validation Network for Object-Level Human Attention Reasoning

Model Selection for Contextual Bandits
http://arxiv.org/pdf/1906.00531v1

Sliced Gromov-Wasserstein
http://arxiv.org/pdf/1905.10124v1

Towards Practical Alternating Least-Squares for CCA

Deep Leakage from Gradients
http://arxiv.org/pdf/1906.08935v1

Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
http://arxiv.org/pdf/1906.11235v1

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
http://arxiv.org/pdf/1906.08763v1

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
http://arxiv.org/pdf/1906.11378v2

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
http://arxiv.org/pdf/1902.08297v2

Semantic-Guided Multi-Attention Localization for Zero-Shot Learning

Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)
http://arxiv.org/pdf/1905.11833v2

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
http://arxiv.org/pdf/1906.03361v3

Computational Separations between Sampling and Optimization

Surfing: Iterative Optimization Over Incrementally Trained Deep Networks
http://arxiv.org/pdf/1907.08653v1

Population-based Meta-Optimizer Guided by Posterior Estimation

On Human-Aligned Risk Minimization

Semi-Parametric Efficient Policy Learning with Continuous Actions
http://arxiv.org/pdf/1905.10116v2

Multi-task Learning for Aggregated Data using Gaussian Processes
http://arxiv.org/pdf/1906.09412v2

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
http://arxiv.org/pdf/1901.08659v1

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
http://arxiv.org/pdf/1903.03605v1

Constraint Augmented Reinforcement Learning for Text-based Recommendation and Generation

Flexible Modeling of Diversity with Strongly Log-Concave Distributions
http://arxiv.org/pdf/1906.05413v1

Momentum-Based Variance Reduction in Non-Convex SGD
http://arxiv.org/pdf/1905.10018v1

Search on the Replay Buffer: Bridging Planning and Reinforcement Learning
http://arxiv.org/pdf/1906.05253v1

Can Unconditional Language Models Recover Arbitrary Sentences?
http://arxiv.org/pdf/1907.04944v1

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
http://arxiv.org/pdf/1906.09458v1

DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging

Certifying Geometric Robustness of Neural Networks

Goal-conditioned Imitation Learning
http://arxiv.org/pdf/1906.05838v2

Robust exploration in linear quadratic reinforcement learning
http://arxiv.org/pdf/1906.01584v1

DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs

Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration
http://arxiv.org/pdf/1905.10680v1

Input-Output Equivalence of Unitary and Contractive RNNs

Hamiltonian Neural Networks
http://arxiv.org/pdf/1906.01563v3

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
http://arxiv.org/pdf/1907.05008v1

Multiple Futures Prediction

Explicitly disentangling image content from translation and rotation with spatial-VAE
http://arxiv.org/pdf/1909.11663v1

A Perspective on False Discovery Rate Control via Knockoffs

A Kernel Loss for Solving the Bellman Equation
http://arxiv.org/pdf/1905.10506v1

Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing
http://arxiv.org/pdf/1801.10242v2

Differential Privacy Has Disparate Impact on Model Accuracy
http://arxiv.org/pdf/1905.12101v1

Riemannian batch normalization for SPD neural networks
http://arxiv.org/pdf/1909.02414v2

Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity

Stacked Capsule Autoencoders
http://arxiv.org/pdf/1906.06818v1

Learning Reward Machines for Partially Observable Reinforcement Learning

Learning Representations by Maximizing Mutual Information Across Views
http://arxiv.org/pdf/1906.00910v2

Learning Deep MRFs with Amortized Bethe Free Energy Minimization

Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity
http://arxiv.org/pdf/1810.07770v2

Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks

Exact Combinatorial Optimization with Graph Convolutional Neural Networks
http://arxiv.org/pdf/1906.01629v2

Fast structure learning with modular regularization
http://arxiv.org/pdf/1706.03353v3

Wasserstein Dependency Measure for Representation Learning
http://arxiv.org/pdf/1903.11780v1

TAB-VCR: Tags and Attributes for Visual Commonsense Reasoning

Universality and individuality in neural dynamics across large populations of recurrent networks
http://arxiv.org/pdf/1907.08549v1

End-to-End Learning on 3D Protein Structure for Interface Prediction

A Family of Robust Stochastic Operators for Reinforcement Learning
http://arxiv.org/pdf/1805.08122v2

Improving Model Robustness and Uncertainty Estimates with Self-Supervised Learning

Inherent Tradeoffs in Learning Fair Representation
http://arxiv.org/pdf/1906.08386v1

Are deep ResNets provably better than linear predictors?
http://arxiv.org/pdf/1907.03922v1

Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
http://arxiv.org/pdf/1906.10720v1

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
http://arxiv.org/pdf/1907.05431v1

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
http://arxiv.org/pdf/1905.08443v1

Approximate Feature Collisions in Neural Nets

Ease-of-Teaching and Language Structure from Emergent Communication
http://arxiv.org/pdf/1906.02403v1

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
http://arxiv.org/pdf/1905.12560v1

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
http://arxiv.org/pdf/1908.07896v2