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Chirality Nets for Human Pose Regression
Uncertainty on Asynchronous Time Event Prediction
Learning Nearest Neighbor Graphs from Noisy Distance Samples
Efficient Symmetric Norm Regression via Linear Sketching
Weighted Linear Bandits for Non-Stationary Environments
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation
On the (In)fidelity and Sensitivity of Explanations
The Label Complexity of Active Learning from Observational Data
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates
Covariate-Powered Empirical Bayes Estimation
On Distributed Averaging for Stochastic k-PCA
Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms
Exact inference in structured prediction
Learning to Propagate for Graph Meta-Learning
Learning to Perform Local Rewriting for Combinatorial Optimization
Generalization Bounds for Neural Networks via Approximate Description Length
Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)
Towards Automatic Concept-based Explanations
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio
Solving graph compression via optimal transport
Quality Aware Generative Adversarial Networks
Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
Precision-Recall Balanced Topic Modelling
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices
When to use parametric models in reinforcement learning?
Globally Optimal Learning for Structured Elliptical Losses
Learning Sparse Distributions using Iterative Hard Thresholding
Variational Bayesian Decision-making for Continuous Utilities
Face Reconstruction from Voice using Generative Adversarial Networks
Variational Bayes under Model Misspecification
Inherent Tradeoffs in Learning Fair Representations
Policy Learning for Fairness in Ranking
Blocking Bandits
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
Deep imitation learning for molecular inverse problems
Verified Uncertainty Calibration
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes
Model Selection for Contextual Bandits
On the Power and Limitations of Random Features for Understanding Neural Networks
Surfing: Iterative Optimization Over Incrementally Trained Deep Networks
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters
Elliptical Perturbations for Differential Privacy
Neural Jump Stochastic Differential Equations
Spherical Text Embedding
Graph Normalizing Flows
Flattening a Hierarchical Clustering through Active Learning
Efficiently escaping saddle points on manifolds
Fast Sparse Group Lasso
Counting the Optimal Solutions in Graphical Models
Optimal Sampling and Clustering in the Stochastic Block Model
Probabilistic Logic Neural Networks for Reasoning
Tensor Monte Carlo: Particle Methods for the GPU era
Shadowing Properties of Optimization Algorithms
Sequence Modeling with Unconstrained Generation Order
Learning Local Search Heuristics for Boolean Satisfiability
A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning
The Randomized Midpoint Method for Log-Concave Sampling
MonoForest framework for tree ensemble analysis
Safe Exploration for Interactive Machine Learning
Exact sampling of determinantal point processes with sublinear time preprocessing
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
On Lazy Training in Differentiable Programming
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
Asymptotics for Sketching in Least Squares Regression
Mapping State Space using Landmarks for Universal Goal Reaching
STREETS: A Novel Camera Network Dataset for Traffic Flow
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning
Fast and Accurate Stochastic Gradient Estimation
Adaptive Influence Maximization with Myopic Feedback
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Fast Efficient Hyperparameter Tuning for Policy Gradient Methods
Batched Multi-armed Bandits Problem
Pseudo-Extended Markov chain Monte Carlo
Adaptive Gradient-Based Meta-Learning Methods
Quaternion Knowledge Graph Embeddings
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling
Channel Gating Neural Networks
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
Causal Regularization
Nonstochastic Multiarmed Bandits with Unrestricted Delays
Ultrametric Fitting by Gradient Descent
Deliberative Explanations: visualizing network insecurities
Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
Adaptive Auxiliary Task Weighting for Reinforcement Learning
Visualizing and Measuring the Geometry of BERT
Learning from Bad Data via Generation
Biases for Emergent Communication in Multi-agent Reinforcement Learning
Outlier-robust estimation of a sparse linear model using $\ell_1$-penalized Huber's $M$-estimator
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
Robustness Verification of Tree-based Models
Levenshtein Transformer
SPoC: Search-based Pseudocode to Code
muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking
Communication trade-offs for Local-SGD with large step size
Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion
Distribution-Independent PAC Learning of Halfspaces with Massart Noise
A Meta-Analysis of Overfitting in Machine Learning
Efficient online learning with kernels for adversarial large scale problems
Generalization Error Analysis of Quantized Compressive Learning
PIDForest: Anomaly Detection via Partial Identification
Learning Reward Machines for Partially Observable Reinforcement Learning
Learning Compositional Neural Programs with Recursive Tree Search and Planning
Efficiently Learning Fourier Sparse Set Functions
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
Universality and individuality in neural dynamics across large populations of recurrent networks
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity
Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing
Reflection Separation using a Pair of Unpolarized and Polarized Images
Recovering Bandits
Combining Generative and Discriminative Models for Hybrid Inference
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
Variational Bayesian Optimal Experimental Design
Cormorant: Covariant Molecular Neural Networks
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits
Cold Case: The Lost MNIST Digits
Robust exploration in linear quadratic reinforcement learning
Bayesian Optimization under Heavy-tailed Payoffs
Convergence of Adversarial Training in Overparametrized Neural Networks
Implicit Posterior Variational Inference for Deep Gaussian Processes
Multi-Criteria Dimensionality Reduction with Applications to Fairness
Assessing Social and Intersectional Biases in Contextualized Word Representations
Learning Positive Functions with Pseudo Mirror Descent
Fast and Provable ADMM for Learning with Generative Priors
Multiagent Evaluation under Incomplete Information
Modeling Conceptual Understanding in Image Reference Games
Calibration tests in multi-class classification: A unifying framework
Theoretical Analysis of Adversarial Learning: A Minimax Approach
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
Adversarial Music: Real world Audio Adversary against Wake-word Detection System
Hindsight Credit Assignment
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
Cross-sectional Learning of Extremal Dependence among Financial Assets
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks
Multilabel reductions: what is my loss optimising?
Smoothing Structured Decomposable Circuits
Private Stochastic Convex Optimization with Optimal Rates
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin
McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds
The Broad Optimality of Profile Maximum Likelihood
Adaptive Density Estimation for Generative Models
Neural Networks with Cheap Differential Operators
On the Downstream Performance of Compressed Word Embeddings
Policy Continuation with Hindsight Inverse Dynamics
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks
Unsupervised Curricula for Visual Meta-Reinforcement Learning
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
Residual Flows for Invertible Generative Modeling
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation
Evaluating Protein Transfer Learning with TAPE
Sequential Neural Processes
Guided Meta-Policy Search
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
Paradoxes in Fair Machine Learning
A unified theory for the origin of grid cells through the lens of pattern formation
Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games
Limitations of Lazy Training of Two-layers Neural Network
This Looks Like That: Deep Learning for Interpretable Image Recognition
Online Learning via the Differential Privacy Lens
Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models
Self-Critical Reasoning for Robust Visual Question Answering
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm
Reconciling meta-learning and continual learning with online mixtures of tasks
Private Learning Implies Online Learning: An Efficient Reduction
Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay
Large Memory Layers with Product Keys
On Exact Computation with an Infinitely Wide Neural Net
Better Transfer Learning with Inferred Successor Maps
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning
List-decodable Linear Regression
An adaptive nearest neighbor rule for classification
Optimal Sparse Decision Trees
Implicit Regularization in Deep Matrix Factorization
Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes
Are sample means in multi-armed bandits positively or negatively biased?
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond
VIREL: A Variational Inference Framework for Reinforcement Learning
Emergence of Object Segmentation in Perturbed Generative Models
On the Hardness of Robust Classification
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
Invertible Convolutional Flow
Wasserstein Weisfeiler-Lehman Graph Kernels
Differentiable Ranking and Sorting using Optimal Transport
Adversarial Training and Robustness for Multiple Perturbations
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization
Scalable Global Optimization via Local Bayesian Optimization
Infra-slow brain dynamics as a marker for cognitive function and decline
Learning by Abstraction: The Neural State Machine
Optimal Stochastic and Online Learning with Individual Iterates
Cross-lingual Language Model Pretraining
Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
DM2C: Deep Mixed-Modal Clustering
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees
Likelihood-Free Overcomplete ICA and Applications In Causal Discovery
A Step Toward Quantifying Independently Reproducible Machine Learning Research
KerGM: Kernelized Graph Matching
Learning dynamic polynomial proofs
On Testing for Biases in Peer Review
Weight Agnostic Neural Networks
When does label smoothing help?
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
Learning Hierarchical Priors in VAEs
A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution
Differentially Private Markov Chain Monte Carlo
Practical Differentially Private Top-k Selection with Pay-what-you-get Composition
Compression with Flows via Local Bits-Back Coding
Learning in Generalized Linear Contextual Bandits with Stochastic Delays
Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium
Perceiving the arrow of time in autoregressive motion
Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG
Implicit Generation and Modeling with Energy Based Models
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
Dual Variational Generation for Low Shot Heterogeneous Face Recognition
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning
Heterogeneous Graph Learning for Visual Commonsense Reasoning
SGD on Neural Networks Learns Functions of Increasing Complexity
Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
Complexity of Highly Parallel Non-Smooth Convex Optimization
Asymmetric Valleys: Beyond Sharp and Flat Local Minima
Conditional Independence Testing using Generative Adversarial Networks
Training Image Estimators without Image Ground Truth
Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models
Positional Normalization
Better Exploration with Optimistic Actor Critic
Quadratic Video Interpolation
Efficient Meta Learning via Minibatch Proximal Update
Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution
Twin Auxilary Classifiers GAN
Identification of Conditional Causal Effects under Markov Equivalence
Finding Friend and Foe in Multi-Agent Games
Learning Perceptual Inference by Contrasting
Point-Voxel CNN for Efficient 3D Deep Learning
Splitting Steepest Descent for Growing Neural Architectures
SySCD: A System-Aware Parallel Coordinate Descent Algorithm
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation
Deep Equilibrium Models
CPM-Nets: Cross Partial Multi-View Networks
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
Ask not what AI can do, but what AI should do: Towards a framework of task delegability
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation
Enabling hyperparameter optimization in sequential autoencoders for spiking neural data
Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling
Adversarial Examples Are Not Bugs, They Are Features
Multi-task Learning for Aggregated Data using Gaussian Processes
Hierarchical Decision Making by Generating and Following Natural Language Instructions
Surround Modulation: A Bio-inspired Connectivity Structure for Convolutional Neural Networks
Self-attention with Functional Time Representation Learning
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
Generalization in multitask deep neural classifiers: a statistical physics approach
On Relating Explanations and Adversarial Examples
On the equivalence between graph isomorphism testing and function approximation with GNNs
Ease-of-Teaching and Language Structure from Emergent Communication
Approximate Feature Collisions in Neural Nets
Abstraction based Output Range Analysis for Neural Networks
Generative Models for Graph-Based Protein Design
The Geometry of Deep Networks: Power Diagram Subdivision
Space and Time Efficient Kernel Density Estimation in High Dimensions
Learning Data Manipulation for Augmentation and Weighting
Gradient-based Adaptive Markov Chain Monte Carlo
Exploring Algorithmic Fairness in Robust Graph Covering Problems
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models
Imitation-Projected Programmatic Reinforcement Learning
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
TAB-VCR: Tags and Attributes based Visual Commonsense Reasoning Baselines
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
Are deep ResNets provably better than linear predictors?
A Family of Robust Stochastic Operators for Reinforcement Learning
End-to-End Learning on 3D Protein Structure for Interface Prediction
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs
Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks
Amortized Bethe Free Energy Minimization for Learning MRFs
A Condition Number for Joint Optimization of Cycle-Consistent Networks
Wasserstein Dependency Measure for Representation Learning
Differential Privacy Has Disparate Impact on Model Accuracy
Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing
Learning Representations by Maximizing Mutual Information Across Views
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration
A Kernel Loss for Solving the Bellman Equation
Stacked Capsule Autoencoders
Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity
Goal-conditioned Imitation Learning
Multiple Futures Prediction
Riemannian batch normalization for SPD neural networks
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
Hamiltonian Neural Networks
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
Explicitly disentangling image content from translation and rotation with spatial-VAE
Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation
Input-Output Equivalence of Unitary and Contractive RNNs
Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning
Certifying Geometric Robustness of Neural Networks
DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging
Can Unconditional Language Models Recover Arbitrary Sentences?
Momentum-Based Variance Reduction in Non-Convex SGD
Reward Constrained Interactive Recommendation with Natural Language Feedback
Flexible Modeling of Diversity with Strongly Log-Concave Distributions
Efficient Rematerialization for Deep Networks
Invariance and identifiability issues for word embeddings
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions
Power analysis of knockoff filters for correlated designs
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences
An Algorithm to Learn Polytree Networks with Hidden Nodes
Semi-Parametric Efficient Policy Learning with Continuous Actions
Function-Space Distributions over Kernels
Beyond the Single Neuron Convex Barrier for Neural Network Certification
Minimal Variance Sampling in Stochastic Gradient Boosting
Compositional Plan Vectors
Computational Separations between Sampling and Optimization
On Human-Aligned Risk Minimization
Locally Private Learning without Interaction Requires Separation
Learning to Optimize in Swarms
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods
Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption
Accurate Layerwise Interpretable Competence Estimation
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
Near Neighbor: Who is the Fairest of Them All?
Offline Contextual Bandits with High Probability Fairness Guarantees
Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis
Regret Bounds for Learning State Representations in Reinforcement Learning
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
PAC-Bayes Un-Expected Bernstein Inequality
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs
Planning with Goal-Conditioned Policies
Generating Diverse High-Fidelity Images with VQ-VAE-2
Don't take it lightly: Phasing optical random projections with unknown operators
Explicit Explore-Exploit Algorithms in Continuous State Spaces
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors
Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm
Unsupervised Co-Learning on $G$-Manifolds Across Irreducible Representations
A Self Validation Network for Object-Level Human Attention Estimation
Thompson Sampling and Approximate Inference
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
Towards Practical Alternating Least-Squares for CCA
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model
Single-Model Uncertainties for Deep Learning
Compiler Auto-Vectorization with Imitation Learning
Abstract Reasoning with Distracting Features
Sliced Gromov-Wasserstein
Pure Exploration with Multiple Correct Answers
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs
Discrete Flows: Invertible Generative Models of Discrete Data
Likelihood Ratios for Out-of-Distribution Detection
Universal Boosting Variational Inference
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets
Thompson Sampling for Multinomial Logit Contextual Bandits
Fast Structured Decoding for Sequence Models
Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization
Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric
Backprop with Approximate Activations for Memory-efficient Network Training
Bayesian Layers: A Module for Neural Network Uncertainty
Hamiltonian descent for composite objectives
DAC: The Double Actor-Critic Architecture for Learning Options
Exact Gaussian Processes on a Million Data Points
On the Fairness of Disentangled Representations
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis
Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees
Region-specific Diffeomorphic Metric Mapping
Policy Poisoning in Batch Reinforcement Learning and Control
Non-Asymptotic Pure Exploration by Solving Games
Flexible information routing in neural populations through stochastic comodulation
Are Disentangled Representations Helpful for Abstract Visual Reasoning?
Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback
Categorized Bandits
Generalization Bounds in the Predict-then-Optimize Framework
Implicit Regularization of Accelerated Methods in Hilbert Spaces
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
General E(2)-Equivariant Steerable CNNs
Robust Attribution Regularization
Structure Learning with Side Information: Sample Complexity
Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces
Efficient characterization of electrically evoked responses for neural interfaces
Differentially Private Distributed Data Summarization under Covariate Shift
Untangling in Invariant Speech Recognition
Outlier Detection and Robust PCA Using a Convex Measure of Innovation
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
Constraint-based Causal Structure Learning with Consistent Separating Sets
Stochastic Frank-Wolfe for Composite Convex Minimization
Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems
A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression
Sample Efficient Active Learning of Causal Trees
Differentially Private Covariance Estimation
Computing Linear Restrictions of Neural Networks
Correlation Priors for Reinforcement Learning
Inducing brain-relevant bias in natural language processing models
User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning
Stochastic Bandits with Context Distributions
Multi-resolution Multi-task Gaussian Processes
A New Perspective on Pool-Based Active Classification and False-Discovery Control
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes
Are Sixteen Heads Really Better than One?
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets
Reinforcement Learning with Convex Constraints
Graph-based Discriminators: Sample Complexity and Expressiveness
Defending Neural Backdoors via Generative Distribution Modeling
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
The Implicit Metropolis-Hastings Algorithm
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift
An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints
Inverting Deep Generative models, One layer at a time
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
Primal-Dual Block Generalized Frank-Wolfe
GOT: An Optimal Transport framework for Graph comparison
Learning Fairness in Multi-Agent Systems
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
Sampled Softmax with Random Fourier Features
A Solvable High-Dimensional Model of GAN
Semi-flat minima and saddle points by embedding neural networks to overparameterization
Using Embeddings to Correct for Unobserved Confounding in Networks
On Robustness to Adversarial Examples and Polynomial Optimization
Adversarial Robustness through Local Linearization
A Graph Theoretic Additive Approximation of Optimal Transport
DiskANN: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node
Towards Hardware-Aware Tractable Learning of Probabilistic Models
No-Regret Learning in Unknown Games with Correlated Payoffs
Learning about an exponential amount of conditional distributions
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems
Towards modular and programmable architecture search
Compacting, Picking and Growing for Unforgetting Continual Learning
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters
Paraphrase Generation with Latent Bag of Words
A New Distribution on the Simplex with Auto-Encoding Applications
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
Alleviating Label Switching with Optimal Transport
RUDDER: Return Decomposition for Delayed Rewards
Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks
Explanations can be manipulated and geometry is to blame
Hierarchical Optimal Transport for Multimodal Distribution Alignment
Object landmark discovery through unsupervised adaptation
On Differentially Private Graph Sparsification and Applications
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions
Metalearned Neural Memory
Recurrent Kernel Networks
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
Variance Reduction in Bipartite Experiments through Correlation Clustering
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes
Shaping Belief States with Generative Environment Models for RL
Exploration via Hindsight Goal Generation
Manifold denoising by Nonlinear Robust Principal Component Analysis
Global Convergence of Gradient Descent for Deep Linear Residual Networks
Diffusion Improves Graph Learning
The continuous Bernoulli: fixing a pervasive error in variational autoencoders
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products
A Fourier Perspective on Model Robustness in Computer Vision
Communication-efficient Distributed SGD with Sketching
Privacy Amplification by Mixing and Diffusion Mechanisms
Episodic Memory in Lifelong Language Learning
Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data
Learning nonlinear level sets for dimensionality reduction in function approximation
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game
Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match
A Simple Baseline for Bayesian Uncertainty in Deep Learning
Online-Within-Online Meta-Learning
Online Convex Matrix Factorization with Representative Regions
Provably robust boosted decision stumps and trees against adversarial attacks
Kernel quadrature with DPPs
Generative Well-intentioned Networks
Fast and Furious Learning in Zero-Sum Games: Vanishing Regret with Non-Vanishing Step Sizes
Sim2real transfer learning for 3D human pose estimation: motion to the rescue
REM: From Structural Entropy to Community Structure Deception
Learning to Correlate in Multi-Player General-Sum Sequential Games
Unified Language Model Pre-training for Natural Language Understanding and Generation
Minimum Stein Discrepancy Estimators
On the Inductive Bias of Neural Tangent Kernels
Self-Supervised Deep Learning on Point Clouds by Reconstructing Space
Piecewise Strong Convexity of Neural Networks
Cross-Domain Transferability of Adversarial Perturbations
Sparse High-Dimensional Isotonic Regression
The Option Keyboard: Combining Skills in Reinforcement Learning
Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves
Making AI Forget You: Data Deletion in Machine Learning
Triad Constraints for Learning Causal Structure of Latent Variables
k-Means Clustering of Lines for Big Data
Recurrent Space-time Graph Neural Networks
Accurate, reliable and fast robustness evaluation
Band-Limited Gaussian Processes: The Sinc Kernel
Streaming Bayesian Inference for Crowdsourced Classification
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification
Unsupervised Object Segmentation by Redrawing
Efficient Algorithms for Smooth Minimax Optimization
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
MetaInit: Initializing learning by learning to initialize
A Generic Acceleration Framework for Stochastic Composite Optimization
Scalable Deep Generative Relational Model with High-Order Node Dependence
Continuous-time Models for Stochastic Optimization Algorithms
Implicit Semantic Data Augmentation for Deep Networks
Learning Hawkes Processes from a handful of events
Random Path Selection for Continual Learning
Selecting causal brain features with a single conditional independence test per feature
Control What You Can: Intrinsically Motivated Task-Planning Agent
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
Beating SGD Saturation with Tail-Averaging and Minibatching
When to Trust Your Model: Model-Based Policy Optimization
Correlation Clustering with Adaptive Similarity Queries
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond
Curriculum-guided Hindsight Experience Replay
Kernelized Bayesian Softmax for Text Generation
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets
A General Framework for Symmetric Property Estimation
Generalization of Reinforcement Learners with Working and Episodic Memory
Classification Accuracy Score for Conditional Generative Models
Screening Sinkhorn Algorithm for Regularized Optimal Transport
Distribution Learning of a Random Spatial Field with a Location-Unaware Mobile Sensor
Structured Prediction with Projection Oracles
Selective Sampling-based Scalable Sparse Subspace Clustering
Tree-Sliced Variants of Wasserstein Distances
Universality in Learning from Linear Measurements
Structured Variational Inference in Continuous Cox Process Models
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning
Root Mean Square Layer Normalization
Integer Discrete Flows and Lossless Compression
Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging
A Primal Dual Formulation For Deep Learning With Constraints
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration
Submodular Function Minimization with Noisy Evaluation Oracle
Multi-objective Bayesian optimisation with preferences over objectives
Novel positional encodings to enable tree-based transformers
Planning in entropy-regularized Markov decision processes and games
Neural Attribution for Semantic Bug-Localization in Student Programs
Debiased Bayesian inference for average treatment effects
Are Labels Required for Improving Adversarial Robustness?
The Impact of Regularization on High-dimensional Logistic Regression
Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach
Bootstrapping Upper Confidence Bound
Attribution-Based Confidence Metric For Deep Neural Networks
Margin-Based Generalization Lower Bounds for Boosted Classifiers
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder
Gradient based sample selection for online continual learning
Graph Transformer Networks
Multivariate Distributionally Robust Convex Regression under Absolute Error Loss
Dimension-Free Bounds for Low-Precision Training
Improved Regret Bounds for Bandit Combinatorial Optimization
Theoretical evidence for adversarial robustness through randomization
Online Continual Learning with Maximal Interfered Retrieval
Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling
Bayesian Optimization with Unknown Search Space
Pareto Multi-Task Learning
Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data
A Domain Agnostic Measure for Monitoring and Evaluating GANs
Learning Auctions with Robust Incentive Guarantees
Concentration of risk measures: A Wasserstein distance approach
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
Thresholding Bandit with Optimal Aggregate Regret
DTWNet: a Dynamic Time Warping Network
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games
Faster Boosting with Smaller Memory
Subquadratic High-Dimensional Hierarchical Clustering
Landmark Ordinal Embedding
Distributed estimation of the inverse Hessian by determinantal averaging
Personalizing Many Decisions with High-Dimensional Covariates
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback
Global Guarantees for Blind Demodulation with Generative Priors
The Thermodynamic Variational Objective
Structured Graph Learning Via Laplacian Spectral Constraints
A Necessary and Sufficient Stability Notion for Adaptive Generalization
Sparse Variational Inference: Bayesian Coresets from Scratch
Demystifying Black-box Models with Symbolic Metamodels
Provable Non-linear Inductive Matrix Completion
Rethinking Kernel Methods for Node Representation Learning on Graphs
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards
Learning Neural Networks with Adaptive Regularization
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport
Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test
Gaussian-Based Pooling for Convolutional Neural Networks
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation
Online EXP3 Learning in Adversarial Bandits with Delayed Feedback
Neural Temporal-Difference Learning Converges to Global Optima
Unlabeled Data Improves Adversarial Robustness
Meta Architecture Search
Greedy Sampling for Approximate Clustering in the Presence of Outliers
Attentive State-Space Modeling of Disease Progression
Region Mutual Information Loss for Semantic Segmentation
Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum
Learning Stable Deep Dynamics Models
Unified Sample-Optimal Property Estimation in Near-Linear Time
Machine Teaching of Active Sequential Learners
On Tractable Computation of Expected Predictions
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Image Captioning: Transforming Objects into Words
Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation
Random Projections with Asymmetric Quantization
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates
MintNet: Building Invertible Neural Networks with Masked Convolutions
Zero-shot Knowledge Transfer via Adversarial Belief Matching
Statistical Model Aggregation via Parameter Matching
How to Initialize your Network? Robust Initialization for WeightNorm & ResNets
An Embedding Framework for Consistent Polyhedral Surrogates
Private Testing of Distributions via Sample Permutations
Exponential Family Estimation via Adversarial Dynamics Embedding
Adversarial Fisher Vectors for Unsupervised Representation Learning
Superposition of many models into one
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
Improving Black-box Adversarial Attacks with a Transfer-based Prior
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals
Online Forecasting of Total-Variation-bounded Sequences
Reducing the variance in online optimization by transporting past gradients
Rates of Convergence for Large-scale Nearest Neighbor Classification
Cross-Modal Learning with Adversarial Samples
High-Dimensional Optimization in Adaptive Random Subspaces
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering
Variational Graph Recurrent Neural Networks
Fast structure learning with modular regularization
Consistency-based Semi-supervised Learning for Object detection
Deep Leakage from Gradients
Worst-Case Regret Bounds for Exploration via Randomized Value Functions
Program Synthesis and Semantic Parsing with Learned Code Idioms
Transfer Learning via Minimizing the Performance Gap Between Domains
Semi-Implicit Graph Variational Auto-Encoders
Unsupervised Learning of Object Keypoints for Perception and Control
Dimensionality reduction: theoretical perspective on practical measures
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks
Learning Multiple Markov Chains via Adaptive Allocation
Learning step sizes for unfolded sparse coding
A Composable Specification Language for Reinforcement Learning Tasks
Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence
Neural Relational Inference with Fast Modular Meta-learning
Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples
Deep Gamblers: Learning to Abstain with Portfolio Theory
Variational Temporal Abstraction
Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy
Efficient Convex Relaxations for Streaming PCA
Sample Complexity of Learning Mixture of Sparse Linear Regressions
Sequential Experimental Design for Transductive Linear Bandits
Discrete Object Generation with Reversible Inductive Construction
Learning Robust Global Representations by Penalizing Local Predictive Power
G2SAT: Learning to Generate SAT Formulas
Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback
Large Scale Adversarial Representation Learning
Same-Cluster Querying for Overlapping Clusters
A unified variance-reduced accelerated gradient method for convex optimization
Limits of Private Learning with Access to Public Data
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Statistical-Computational Tradeoff in Single Index Models
Bandits with Feedback Graphs and Switching Costs
On the Expressive Power of Deep Polynomial Neural Networks
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models
Superset Technique for Approximate Recovery in One-Bit Compressed Sensing
Certainty Equivalence is Efficient for Linear Quadratic Control
KNG: The K-Norm Gradient Mechanism
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards
MarginGAN: Adversarial Training in Semi-Supervised Learning
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.
Can SGD Learn Recurrent Neural Networks with Provable Generalization?
CXPlain: Causal Explanations for Model Interpretation under Uncertainty
Learning to Self-Train for Semi-Supervised Few-Shot Classification
Functional Adversarial Attacks
A Game Theoretic Approach to Class-wise Selective Rationalization
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning
Efficiently avoiding saddle points with zero order methods: No gradients required
SHE: A Fast and Accurate Deep Neural Network for Encrypted Data
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization
On Fenchel Mini-Max Learning
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
Learning metrics for persistence-based summaries and applications for graph classification
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization
Unsupervised Meta-Learning for Few-Shot Image Classification
Learning Mixtures of Plackett-Luce Models from Structured Partial Orders
Metamers of neural networks reveal divergence from human perceptual systems
Efficient Forward Architecture Search
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation
Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes
A neurally plausible model for online recognition and postdiction in a dynamical environment
Dying Experts: Efficient Algorithms with Optimal Regret Bounds
Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization
Multi-Agent Common Knowledge Reinforcement Learning
A Benchmark for Interpretability Methods in Deep Neural Networks
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning
Average Case Column Subset Selection for Entrywise $\ell_1$-Norm Loss
Learning to Learn By Self-Critique
Model Similarity Mitigates Test Set Overuse
Decentralized sketching of low rank matrices
Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond
Transductive Zero-Shot Learning with Visual Structure Constraint
Contextual Bandits with Cross-Learning
On the Value of Target Data in Transfer Learning
Meta Learning with Relational Information for Short Sequences
Bayesian Joint Estimation of Multiple Graphical Models
Compositional generalization through meta sequence-to-sequence learning
Lookahead Optimizer: k steps forward, 1 step back
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons
Understanding the Role of Momentum in Stochastic Gradient Methods
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models
Memory Efficient Adaptive Optimization
Practical Two-Step Lookahead Bayesian Optimization
Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions
Using Statistics to Automate Stochastic Optimization
Certified Adversarial Robustness with Additive Noise
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
Differentiable Convex Optimization Layers
A Bayesian Theory of Conformity in Collective Decision Making
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization
Learning from brains how to regularize machines
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks
Random Tessellation Forests
Sobolev Independence Criterion
Maximum Entropy Monte-Carlo Planning
Non-Cooperative Inverse Reinforcement Learning
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
Language as an Abstraction for Hierarchical Deep Reinforcement Learning
ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias
Multiclass Performance Metric Elicitation
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse
Deep Generative Video Compression
Discovery of Useful Questions as Auxiliary Tasks
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models
Correlation clustering with local objectives
Correlation in Extensive-Form Games: Saddle-Point Formulation and Benchmarks
Linear Stochastic Bandits Under Safety Constraints
A coupled autoencoder approach for multi-modal analysis of cell types
A Stochastic Composite Gradient Method with Incremental Variance Reduction
Budgeted Reinforcement Learning in Continuous State Space
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback
Distributionally Robust Optimization and Generalization in Kernel Methods
Sampling Networks and Aggregate Simulation for Online POMDP Planning
Defending Against Neural Fake News
GNNExplainer: Generating Explanations for Graph Neural Networks
A General Theory of Equivariant CNNs on Homogeneous Spaces
(Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs
Write, Execute, Assess: Program Synthesis with a REPL
Sample Adaptive MCMC
Learning Bayesian Networks with Low Rank Conditional Probability Tables
STAR-Caps: Capsule Networks with Straight-Through Attentive Routing
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration
What Can ResNet Learn Efficiently, Going Beyond Kernels?
A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers
Trivializations for Gradient-Based Optimization on Manifolds
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks
PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points
Adaptively Aligned Image Captioning via Adaptive Attention Time
PRNet: Self-Supervised Learning for Partial-to-Partial Registration
Surrogate Objectives for Batch Policy Optimization in One-step Decision Making
Unlocking Fairness: a Trade-off Revisited
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Approximating the Permanent by Sampling from Adaptive Partitions
Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network
Graph Structured Prediction Energy Networks
Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems
Fisher Efficient Inference of Intractable Models
Unsupervised State Representation Learning in Atari
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations
Learning to Screen
Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians
Latent distance estimation for random geometric graphs
Graph Agreement Models for Semi-Supervised Learning
Retrosynthesis Prediction with Conditional Graph Logic Network
Recurrent Registration Neural Networks for Deformable Image Registration
Finite-Sample Analysis for SARSA with Linear Function Approximation
Equal Opportunity in Online Classification with Partial Feedback
A Little Is Enough: Circumventing Defenses For Distributed Learning
Learning Deterministic Weighted Automata with Queries and Counterexamples
Neural Multisensory Scene Inference
A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator
Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory
The Functional Neural Process
Facility Location Problem in Differential Privacy Model Revisited
A Universally Optimal Multistage Accelerated Stochastic Gradient Method
Learning from Trajectories via Subgoal Discovery
Multiclass Learning from Contradictions
Distributed Low-rank Matrix Factorization With Exact Consensus
Energy-Inspired Models: Learning with Sampler-Induced Distributions
An adaptive Mirror-Prox method for variational inequalities with singular operators
The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
Robust and Communication-Efficient Collaborative Learning
Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost
Online Normalization for Training Neural Networks
Manipulating a Learning Defender and Ways to Counteract
Fixing the train-test resolution discrepancy
Certifiable Robustness to Graph Perturbations
Fast Decomposable Submodular Function Minimization using Constrained Total Variation
Hyperbolic Graph Neural Networks
The spiked matrix model with generative priors
Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations
Gradient Dynamics of Shallow Univariate ReLU Networks
Möbius Transformation for Fast Inner Product Search on Graph
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes
Learning to Infer Implicit Surfaces without 3D Supervision
Learning Distributions Generated by One-Layer ReLU Networks
Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds
Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle
Large-scale optimal transport map estimation using projection pursuit
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
Dynamic Local Regret for Non-convex Online Forecasting
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning
Provably Efficient Q-Learning with Low Switching Cost
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms
Differentially Private Anonymized Histograms
A Debiased MDI Feature Importance Measure for Random Forests
Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes
A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening
Post training 4-bit quantization of convolutional networks for rapid-deployment
Max-value Entropy Search for Multi-Objective Bayesian Optimization
Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks
Detecting Overfitting via Adversarial Examples
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment
Towards Understanding the Importance of Shortcut Connections in Residual Networks
Stein Variational Gradient Descent With Matrix-Valued Kernels
A Model to Search for Synthesizable Molecules
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies
Stability of Graph Scattering Transforms
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space
Re-examination of the Role of Latent Variables in Sequence Modeling
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
How degenerate is the parametrization of neural networks with the ReLU activation function?
The Implicit Bias of AdaGrad on Separable Data
Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards
Coresets for Clustering with Fairness Constraints
LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition
Continual Unsupervised Representation Learning
MAVEN: Multi-Agent Variational Exploration
On two ways to use determinantal point processes for Monte Carlo integration
Solving Interpretable Kernel Dimensionality Reduction
Constrained Reinforcement Learning Has Zero Duality Gap
Foundations of Comparison-Based Hierarchical Clustering
Lower Bounds on Adversarial Robustness from Optimal Transport
Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization
Competitive Gradient Descent
The Parameterized Complexity of Cascading Portfolio Scheduling
Self-Routing Capsule Networks
What the Vec? Towards Probabilistically Grounded Embeddings
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Nonlinear scaling of resource allocation in sensory bottlenecks
Minimizers of the Empirical Risk and Risk Monotonicity
PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments
Explicit Planning for Efficient Exploration in Reinforcement Learning
Normalization Helps Training of Quantized LSTM
GRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series
Neural Spline Flows
Coresets for Archetypal Analysis
Nonzero-sum Adversarial Hypothesis Testing Games
Learning elementary structures for 3D shape generation and matching
Estimating Convergence of Markov chains with L-Lag Couplings
Deep Scale-spaces: Equivariance Over Scale
Escaping from saddle points on Riemannian manifolds
Universal Invariant and Equivariant Graph Neural Networks
Modeling Tabular data using Conditional GAN
Localized Structured Prediction
Learning-Based Low-Rank Approximations
Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
Toward a Characterization of Loss Functions for Distribution Learning
Manifold-regression to predict from MEG/EEG brain signals without source modeling
Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning
Multi-source Domain Adaptation for Semantic Segmentation
On the Correctness and Sample Complexity of Inverse Reinforcement Learning
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation
Learning from Label Proportions with Generative Adversarial Networks
Robust Principal Component Analysis with Adaptive Neighbors
On the convergence of single-call stochastic extra-gradient methods
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off
First Order Motion Model for Image Animation
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs
Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem
Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
High-Quality Self-Supervised Deep Image Denoising
Discriminator optimal transport
Online Prediction of Switching Graph Labelings with Cluster Specialists
Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs
GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs
Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion
Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent
Graph-Based Semi-Supervised Learning with Non-ignorable Non-response
Quantum Wasserstein Generative Adversarial Networks
Are Anchor Points Really Indispensable in Label-Noise Learning?
Learning Nonsymmetric Determinantal Point Processes
Fast AutoAugment
Interval timing in deep reinforcement learning agents
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior
Efficient Pure Exploration in Adaptive Round Model
Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time
High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes
Multi-objects Generation with Amortized Structural Regularization
Discriminative Topic Modeling with Logistic LDA
Semi-supervisedly Co-embedding Attributed Networks
Hyperparameter Learning via Distributional Transfer
DetNAS: Backbone Search for Object Detection
Oblivious Sampling Algorithms for Private Data Analysis
Is Deeper Better only when Shallow is Good?
First-order methods almost always avoid saddle points: The case of vanishing step-sizes
Large Scale Structure of Neural Network Loss Landscapes
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI
Maximum Mean Discrepancy Gradient Flow
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders
Code Generation as a Dual Task of Code Summarization
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
Hypothesis Set Stability and Generalization
Bayesian Batch Active Learning as Sparse Subset Approximation
Diffeomorphic Temporal Alignment Nets
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
Copula Multi-label Learning
Domain Generalization via Model-Agnostic Learning of Semantic Features
Bayesian Learning of Sum-Product Networks
The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks
On the Convergence Rate of Training Recurrent Neural Networks
Grid Saliency for Context Explanations of Semantic Segmentation
Anti-efficient encoding in emergent communication
Convergence Guarantees for Adaptive Bayesian Quadrature Methods
Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation
Singleshot : a scalable Tucker tensor decomposition
Mining GOLD Samples for Conditional GANs
Reliable training and estimation of variance networks
L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise
Meta-Surrogate Benchmarking for Hyperparameter Optimization
Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder
Progressive Augmentation of GANs
Fully Parameterized Quantile Function for Distributional Reinforcement Learning
Neural Machine Translation with Soft Prototype
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
Constrained deep neural network architecture search for IoT devices accounting for hardware calibration
Towards a Zero-One Law for Column Subset Selection
Deep Model Transferability from Attribution Maps
Iterative Least Trimmed Squares for Mixed Linear Regression
Intrinsic dimension of data representations in deep neural networks
Distributional Reward Decomposition for Reinforcement Learning
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning
Compositional De-Attention Networks
Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces
Divergence-Augmented Policy Optimization
Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning
Comparing Unsupervised Word Translation Methods Step by Step
Equipping Experts/Bandits with Long-term Memory
Scalable inference of topic evolution via models for latent geometric structures
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer
Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs
Doubly-Robust Lasso Bandit
Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model
Optimal Best Markovian Arm Identification with Fixed Confidence
Deep Active Learning with a Neural Architecture Search
Co-Generation with GANs using AIS based HMC
Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently
In-Place Zero-Space Memory Protection for CNN
Learning GANs and Ensembles Using Discrepancy
Connective Cognition Network for Directional Visual Commonsense Reasoning
MaCow: Masked Convolutional Generative Flow
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network
Mixtape: Breaking the Softmax Bottleneck Efficiently
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
Comparison Against Task Driven Artificial Neural Networks Reveals Functional Organization of Mouse Visual Cortex
Topology-Preserving Deep Image Segmentation
Variance Reduced Policy Evaluation with Smooth Function Approximation
Learning Disentangled Representations for Recommendation
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
A Latent Variational Framework for Stochastic Optimization
Acceleration via Symplectic Discretization of High-Resolution Differential Equations
Limiting Extrapolation in Linear Approximate Value Iteration
Markov Random Fields for Collaborative Filtering
Ouroboros: On Accelerating Training of Transformer-Based Language Models
Quantum Embedding of Knowledge for Reasoning
Focused Quantization for Sparse CNNs
Regularized Gradient Boosting
Robustness to Adversarial Perturbations in Learning from Incomplete Data
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
A Refined Margin Distribution Analysis for Forest Representation Learning
Time-series Generative Adversarial Networks
Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks
Off-Policy Evaluation via Off-Policy Classification
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
Input Similarity from the Neural Network Perspective
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
Characterizing Bias in Classifiers using Generative Models
Balancing Efficiency and Fairness in On-Demand Ridesourcing
Adaptive Sequence Submodularity
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Incremental Few-Shot Learning with Attention Attractor Networks
Learning Disentangled Representation for Robust Person Re-identification
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
Estimating Entropy of Distributions in Constant Space
On the Accuracy of Influence Functions for Measuring Group Effects
On the Utility of Learning about Humans for Human-AI Coordination
Optimistic Regret Minimization for Extensive-Form Games via Dilated Distance-Generating Functions
Fast Parallel Algorithms for Statistical Subset Selection Problems
Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model
E2-Train: Training State-of-the-art CNNs with Over 80% Less Energy
On the number of variables to use in principal component regression
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Visual Concept-Metaconcept Learning
Data-driven Estimation of Sinusoid Frequencies
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs
PHYRE: A New Benchmark for Physical Reasoning
Neural Similarity Learning
Prior-Free Dynamic Auctions with Low Regret Buyers
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Multivariate Triangular Quantile Maps for Novelty Detection
ANODEV2: A Coupled Neural ODE Framework
Learning Mean-Field Games
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery
Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities
Spectral Modification of Graphs for Improved Spectral Clustering
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies
Fair Algorithms for Clustering
Few-shot Video-to-Video Synthesis
Policy Evaluation with Latent Confounders via Optimal Balance
MixMatch: A Holistic Approach to Semi-Supervised Learning
Mutually Regressive Point Processes
Ordered Memory
Unsupervised Scalable Representation Learning for Multivariate Time Series
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
Hyperbolic Graph Convolutional Neural Networks
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers
A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI
Making the Cut: A Bandit-based Approach to Tiered Interviewing
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes
Efficient Deep Approximation of GMMs
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments
Adaptive Cross-Modal Few-shot Learning
On Single Source Robustness in Deep Fusion Models
Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks
Game Design for Eliciting Distinguishable Behavior
Cost Effective Active Search
Offline Contextual Bayesian Optimization
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces
End to end learning and optimization on graphs
Partially Encrypted Deep Learning using Functional Encryption
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation
State Aggregation Learning from Markov Transition Data
Multi-relational Poincaré Graph Embeddings
Disentangling Influence: Using disentangled representations to audit model predictions
Double Quantization for Communication-Efficient Distributed Optimization
Decentralized Cooperative Stochastic Bandits
Globally optimal score-based learning of directed acyclic graphs in high-dimensions
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
Learning low-dimensional state embeddings and metastable clusters from time series data
Massively scalable Sinkhorn distances via the Nyström method
No-Press Diplomacy: Modeling Multi-Agent Gameplay
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning
Uncertainty-based Continual Learning with Adaptive Regularization
Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
Understanding and Improving Layer Normalization
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks
Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms
A Geometric Perspective on Optimal Representations for Reinforcement Learning
Learning Deep Bilinear Transformation for Fine-grained Image Representation
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections
On Adversarial Mixup Resynthesis
Flow-based Image-to-Image Translation with Feature Disentanglement
Training Language GANs from Scratch
Limitations of the empirical Fisher approximation for natural gradient descent
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
Data Cleansing for Models Trained with SGD
Practical Deep Learning with Bayesian Principles
Embedding Symbolic Knowledge into Deep Networks
A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models
Curvilinear Distance Metric Learning
Approximation Ratios of Graph Neural Networks for Combinatorial Problems
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
Thinning for Accelerating the Learning of Point Processes
A First-Order Algorithmic Framework for Wasserstein Distributionally Robust Logistic Regression
Full-Gradient Representation for Neural Network Visualization
Regularized Weighted Low Rank Approximation
Understanding Attention and Generalization in Graph Neural Networks
Efficient Graph Generation with Graph Recurrent Attention Networks
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits
Bat-G net: Bat-inspired High-Resolution 3D Image Reconstruction using Ultrasonic Echoes
q-means: A quantum algorithm for unsupervised machine learning
Improved Precision and Recall Metric for Assessing Generative Models
Cross Attention Network for Few-shot Classification
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization
Teaching Multiple Concepts to a Forgetful Learner
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction
Learning Generalizable Device Placement Algorithms for Distributed Machine Learning
Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso
PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph
Uncoupled Regression from Pairwise Comparison Data
Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD
Learning Sample-Specific Models with Low-Rank Personalized Regression
Learning Representations for Time Series Clustering
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
Exact Rate-Distortion in Autoencoders via Echo Noise
Conformalized Quantile Regression
Practical and Consistent Estimation of f-Divergences
Ultra Fast Medoid Identification via Correlated Sequential Halving
Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks
Predicting the Politics of an Image Using Webly Supervised Data
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk
Adaptive GNN for Image Analysis and Editing
Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling
Dancing to Music
LCA: Loss Change Allocation for Neural Network Training
Thompson Sampling with Information Relaxation Penalties
Coda: An End-to-End Neural Program Decompiler
Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds
Deep Generalized Method of Moments for Instrumental Variable Analysis
Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration
Capacity Bounded Differential Privacy
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
First order expansion of convex regularized estimators
Implicitly learning to reason in first-order logic
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods
Meta-Curvature
Adversarial training for free!
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning
Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients
Optimal Decision Tree with Noisy Outcomes
Transfusion: Understanding Transfer Learning for Medical Imaging
A Flexible Generative Framework for Graph-based Semi-supervised Learning
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
Deep Set Prediction Networks
DppNet: Approximating Determinantal Point Processes with Deep Networks
Distinguishing Distributions When Samples Are Strategically Transformed
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks
Fully Dynamic Consistent Facility Location
Neural Lyapunov Control
Augmented Neural ODEs
Deep Signature Transforms
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition
Convergent Policy Optimization for Safe Reinforcement Learning
Approximate Inference Turns Deep Networks into Gaussian Processes
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
Inherent Weight Normalization in Stochastic Neural Networks
Learning Temporal Pose Estimation from Sparsely-Labeled Videos
Implicit Regularization for Optimal Sparse Recovery
Real-Time Reinforcement Learning
Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function
Multi-mapping Image-to-Image Translation via Learning Disentanglement
Addressing Failure Prediction by Learning Model Confidence
Fooling Neural Network Interpretations via Adversarial Model Manipulation
Copula-like Variational Inference
Backpropagation-Friendly Eigendecomposition
FastSpeech: Fast, Robust and Controllable Text to Speech
Robust Multi-agent Counterfactual Prediction
Locally Private Gaussian Estimation
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits
Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels
PAC-Bayes under potentially heavy tails
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems
Identifying Causal Effects via Context-specific Independence Relations
A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods
Regularizing Trajectory Optimization with Denoising Autoencoders
Bridging Machine Learning and Logical Reasoning by Abductive Learning
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components
One-Shot Object Detection with Co-Attention and Co-Excitation
Knowledge Extraction with No Observable Data
Combinatorial Bayesian Optimization using the Graph Cartesian Product
Glyce: Glyph-vectors for Chinese Character Representations
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems
Discovering Neural Wirings
On the Calibration of Multiclass Classification with Rejection
Conformal Prediction Under Covariate Shift
Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective
Interlaced Greedy Algorithm for Maximization of Submodular Functions in Nearly Linear Time
Information-Theoretic Confidence Bounds for Reinforcement Learning
Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation
Total Least Squares Regression in Input Sparsity Time
Learning Robust Options by Conditional Value at Risk Optimization
Large Scale Markov Decision Processes with Changing Rewards
Hyper-Graph-Network Decoders for Block Codes
Transfer Anomaly Detection by Inferring Latent Domain Representations
Positive-Unlabeled Compression on the Cloud
SpiderBoost and Momentum: Faster Variance Reduction Algorithms
Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller
Stagewise Training Accelerates Convergence of Testing Error Over SGD
Multiview Aggregation for Learning Category-Specific Shape Reconstruction
Learning Transferable Graph Exploration
Gradient Information for Representation and Modeling
Adapting Neural Networks for the Estimation of Treatment Effects
Direct Estimation of Differential Functional Graphical Models
Semi-Parametric Dynamic Contextual Pricing
Initialization of ReLUs for Dynamical Isometry
Kernel Stein Tests for Multiple Model Comparison
Rethinking the CSC Model for Natural Images
Disentangled behavioural representations
More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation
Deep Structured Prediction for Facial Landmark Detection
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning
Park: An Open Platform for Learning-Augmented Computer Systems
Partitioning Structure Learning for Segmented Linear Regression Trees
Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function
Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update
Information Competing Process for Learning Diversified Representations
Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases
Order Optimal One-Shot Distributed Learning
Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design
Controllable Text-to-Image Generation
Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach
Improving Textual Network Learning with Variational Homophilic Embeddings
Controlling Neural Level Sets
CNN^{2}: Viewpoint Generalization via a Binocular Vision
GENO -- GENeric Optimization for Classical Machine Learning
Fully Neural Network based Model for General Temporal Point Processes
Provably Powerful Graph Networks
A Tensorized Transformer for Language Modeling
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning
Blended Matching Pursuit
Neural networks grown and self-organized by noise
Nonconvex Low-Rank Symmetric Tensor Completion from Noisy Data
XNAS: Neural Architecture Search with Expert Advice
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
Variational Structured Semantic Inference for Diverse Image Captioning
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks
Multi-marginal Wasserstein GAN
On the Curved Geometry of Accelerated Optimization
Self-Supervised Generalisation with Meta Auxiliary Learning
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers
An Improved Analysis of Training Over-parameterized Deep Neural Networks
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction
Importance Resampling for Off-policy Prediction
Meta-Learning Representations for Continual Learning
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Generalized Off-Policy Actor-Critic
Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes
Variational Denoising Network: Toward Blind Noise Modeling and Removal
Learnable Tree Filter for Structure-preserving Feature Transform
Unconstrained Monotonic Neural Networks
Random deep neural networks are biased towards simple functions
Incremental Scene Synthesis
Coordinated hippocampal-entorhinal replay as structural inference
Visualizing the PHATE of Neural Networks
SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
Data-Dependence of Plateau Phenomenon in Learning with Neural Network --- Statistical Mechanical Analysis
Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions
Expressive power of tensor-network factorizations for probabilistic modeling
Hierarchical Optimal Transport for Document Representation
Hyperspherical Prototype Networks
Neural Diffusion Distance for Image Segmentation
Fine-grained Optimization of Deep Neural Networks
Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images
Computing Full Conformal Prediction Set with Approximate Homotopy
Multi-View Reinforcement Learning
Sampling Sketches for Concave Sublinear Functions of Frequencies
Distributional Policy Optimization: An Alternative Approach for Continuous Control
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards
On The Classification-Distortion-Perception Tradeoff
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
CondConv: Conditionally Parameterized Convolutions for Efficient Inference
Conditional Structure Generation through Graph Variational Generative Adversarial Nets
Online sampling from log-concave distributions
Model Compression with Adversarial Robustness: A Unified Optimization Framework
Gated CRF Loss for Weakly Supervised Semantic Image Segmentation
Cross-channel Communication Networks
Image Synthesis with a Single (Robust) Classifier
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up
Envy-Free Classification
Regression Planning Networks
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence
Convolution with even-sized kernels and symmetric padding
Combinatorial Bandits with Relative Feedback
Reconciling λ-Returns with Experience Replay
A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation
Explicit Disentanglement of Appearance and Perspective in Generative Models
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme
Selecting the independent coordinates of manifolds with large aspect ratios
Polynomial Cost of Adaptation for X-Armed Bandits
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes
Combinatorial Inference against Label Noise
Powerset Convolutional Neural Networks
Deep Supervised Summarization: Algorithm and Application to Learning Instructions
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data
Memory-oriented Decoder for Light Field Salient Object Detection
Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels
Deep Learning without Weight Transport
DATA: Differentiable ArchiTecture Approximation
Network Pruning via Transformable Architecture Search
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums
Saccader: Improving Accuracy of Hard Attention Models for Vision
Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition
Volumetric Correspondence Networks for Optical Flow
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning
Importance Weighted Hierarchical Variational Inference
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling
Differentially Private Bayesian Linear Regression
DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution
Secretary Ranking with Minimal Inversions
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition
Trust Region-Guided Proximal Policy Optimization
Differentiable Cloth Simulation for Inverse Problems
RSN: Randomized Subspace Newton
NAT: Neural Architecture Transformer for Accurate and Compact Architectures
No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms
Multiway clustering via tensor block models
Adversarial Self-Defense for Cycle-Consistent GANs
RUBi: Reducing Unimodal Biases for Visual Question Answering
Zero-Shot Semantic Segmentation
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation
Towards closing the gap between the theory and practice of SVRG
ETNet: Error Transition Network for Arbitrary Style Transfer
Poisson-Randomized Gamma Dynamical Systems
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
Equitable Stable Matchings in Quadratic Time
Learning Erdos-Renyi Random Graphs via Edge Detecting Queries
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos
Invert to Learn to Invert
Metric Learning for Adversarial Robustness
Chasing Ghosts: Instruction Following as Bayesian State Tracking
Learning Conditional Deformable Templates with Convolutional Networks
Block Coordinate Regularization by Denoising
Reducing Noise in GAN Training with Variance Reduced Extragradient
A Primal-Dual link between GANs and Autoencoders
Provable Gradient Variance Guarantees for Black-Box Variational Inference
Deep ReLU Networks Have Surprisingly Few Activation Patterns
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians
Noise-tolerant fair classification
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
Experience Replay for Continual Learning
Joint-task Self-supervised Learning for Temporal Correspondence
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
Generalized Sliced Wasserstein Distances
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation
Meta-Learning with Implicit Gradients
Zero-shot Learning via Simultaneous Generating and Learning
DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision
The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection
Multi-Resolution Weak Supervision for Sequential Data
Stand-Alone Self-Attention in Vision Models
Private Hypothesis Selection
FreeAnchor: Learning to Match Anchors for Visual Object Detection
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Unsupervised learning of object structure and dynamics from videos
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers
Faster width-dependent algorithm for mixed packing and covering LPs
Exponentially convergent stochastic k-PCA without variance reduction
Causal Confusion in Imitation Learning
Necessary and Sufficient Geometries for Gradient Methods
Geometry-Aware Neural Rendering
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
Generative Modeling by Estimating Gradients of the Data Distribution
R2D2: Reliable and Repeatable Detector and Descriptor
Understanding Sparse JL for Feature Hashing
Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration
Uniform convergence may be unable to explain generalization in deep learning
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
Average Individual Fairness: Algorithms, Generalization and Experiments
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks
A neurally plausible model learns successor representations in partially observable environments
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video
Optimizing Generalized Rate Metrics with Three Players
Variance Reduction for Matrix Games
On Making Stochastic Classifiers Deterministic
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models
On Robustness of Principal Component Regression
Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses
Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input
Logarithmic Regret for Online Control
Fast and Accurate Least-Mean-Squares Solvers
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Efficient and Thrifty Voting by Any Means Necessary
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup
Kernel Instrumental Variable Regression
Putting An End to End-to-End: Gradient-Isolated Learning of Representations
Blind Super-Resolution Kernel Estimation using an Internal-GAN
Parameter elimination in particle Gibbs sampling
Guided Similarity Separation for Image Retrieval
HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models
Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations
Strategizing against No-regret Learners
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