no_used_paper.txt

Ask not what AI can do for you, but what AI should do: Towards a framework of task delegability
TensorPipe: Easy Scaling with Micro-Batch Pipeline Parallelism
Differentially Private Hypothesis Selection
New Differentially Private Algorithms for Learning Mixtures of Well-Separated Gaussians
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
Generalized Sliced Wasserstein Distances
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
Joint-task Self-supervised Learning for Temporal Correspondence
Provable Gradient Variance Guarantees for Black-Box Variational Inference
Experience Replay for Continual Learning
Invert to Learn to Invert
Equitable Stable Matchings in Quadratic Time
Batched Multi-armed Bandits Problem
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis
SySCD: A System-Aware Parallel Coordinate Descent Algorithm
Importance Weighted Hierarchical Variational Inference
RSN: Randomized Subspace Newton
Towards closing the gap between the theory and practice of SVRG
No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms
Deep Equilibrium Models
Differentiable Cloth Simulation for Inverse Problems
Poisson-randomized Gamma Dynamical Systems
RUBi: Reducing Unimodal Biases in Visual Question Answering
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution
Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums
Deep Learning without Weight Transport
Combinatorial Bandits with Relative Feedback 
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme
Polynomial Cost of Adaptation for X-Armed Bandits
Secretary Ranking with Minimal Inversions
Learning Perceptual Inference by Contrasting
Selecting the independent coordinates of manifolds with large aspect ratios
Region-specific Diffeomorphic Metric Mapping
Subset Selection via Supervised Facility Location
Reconciling λ-Returns with Experience Replay
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs
Combinatorial Inference against Label Noise
Convolution with even-sized kernels and symmetric padding
Finding Friend and Foe in Multi-Agent Games
Computer Vision with a Single (Robust) Classifier
CondConv: Conditionally Parameterized Convolutions for Efficient Inference
Sampling Sketches for Concave Sublinear Functions of Frequencies
Computing Full Conformal Prediction Set with Approximate Homotopy
Wibergian Learning of Continuous Energy Functions
SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points
Efficient Meta Learning via Minibatch Proximal Update
Guided Similarity Separation for Image Retrieval
Strategizing against No-regret Learners
Positional Normalization
Incremental Scene Synthesis
Self-Supervised Generalisation with Meta Auxiliary Learning
Fast Sparse Group Lasso
Coordinated hippocampal-entorhinal replay as structural inference
Better Exploration with Optimistic Actor Critic
Importance Resampling for Off-policy Prediction
The Label Complexity of Active Learning from Observational Data
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning
Variational Structured Semantic Inference for Diverse Image Captioning
Mapping State Space using Landmarks for Universal Goal Reaching
 Generalized Off-Policy Actor-Critic
Controlling Neural Level Sets
Blended Matching Pursuit
The Randomized Midpoint Method for Log-Concave Sampling
Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design
Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function
Kernel Stein Tests for Multiple Model Comparison
Rethinking the CSC Model for Natural Images
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
Perceiving the arrow of time in autoregressive motion
Nearly Linear-Time, Deterministic Algorithm for Maximizing (Non-Monotone) Submodular Functions Under Cardinality Constraint
Initialization of ReLUs for Dynamical Isometry
SpiderBoost and Momentum: Faster Variance Reduction Algorithms
Minimax rates of estimating approximate differential privacy
Training Image Estimators without Image Ground Truth
Park: An Open Platform for Learning-Augmented Computer Systems
Conformal Prediction Under Covariate Shift
Asymmetric Valleys: Beyond Sharp and Flat Local Minima
Third-Person Visual Imitation Learning via Decoupled Hierarchical Control
Stagewise Training Accelerates Convergence of Testing Error Over SGD
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems
Discovering Neural Wirings
Knowledge Extraction with No Observable Data
Turbo Autoencoder: Deep learning based channel code for point-to-point communication channels
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods
Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits
On Lazy Training in Differentiable Programming
Copula-like Variational Inference
Implicit Regularization for Optimal Sparse Recovery
Locally Private Gaussian Estimation
Multi-mapping Image-to-Image Translation via Learning Disentanglement
Structured Decoding for Non-Autoregressive Machine Translation
Robust Multi-agent Counterfactual Prediction
Deep Signatures
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits
Augmented Neural ODEs
Backpropagation-Friendly Eigendecomposition
Ultrametric Fitting by Gradient Descent
Neural Lyapunov Control
Fully Dynamic Consistent Facility Location
Meta-Curvature
Transfusion: Understanding Transfer Learning for Medical Imaging
Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients
Implicitly learning to reason in first-order logic
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds
First order expansion of convex regularized estimators
Capacity Bounded Differential Privacy
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk
Practical Differentially Private Top-k Selection with Pay-what-you-get Composition
Thompson Sampling with Information Relaxation Penalties
Deep Generalized Method of Moments for Instrumental Variable Analysis
Dance to Music
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask
Who Learns? Decomposing Learning into Per-Parameter Loss Contribution
Predicting the Politics of an Image Using Webly Supervised Data
Ultra Fast Medoid Identification via Correlated Sequential Halving
Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
Exact inference in structured prediction
Coda: An End-to-End Neural Program Decompiler
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
Variance Reduced Uncertainty Calibration
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling
Cross-sectional Learning of Extremal Dependence among Financial Assets
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI
Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise
A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution
Teaching Multiple Concepts to a Forgetful Learner
Practical and Consistent Estimation of f-Divergences
Thinning for Accelerating the Learning of Point Processes
Limitations of the empirical Fisher approximation
Learning dynamic semi-algebraic proofs
Shape and Time Distorsion Loss for Training Deep Time Series Forecasting Models
Data Cleansing for Models Trained with SGD
Modeling Uncertainty by Learning A Hierarchy of Deep Neural Connections
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms
Understanding and Improving Layer Normalization
Uncertainty-based Continual Learning with Adaptive Regularization
Massively scalable Sinkhorn distances via the Nyström method
No-Press Diplomacy: Modeling Multi-Agent Gameplay
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
Partially Encrypted Deep Learning using Functional Encryption
Decentralized Cooperative Stochastic Bandits
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
Efficient Deep Approximation of GMMs
Fast Efficient Hyperparameter Tuning for Policy Gradient Methods
Making the Cut: A Bandit-based Approach to Tiered Interviewing
A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI
Game Design for Eliciting Distinguishable Behavior
When does label smoothing help?
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Blocking Bandits
Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities
Prior-Free Dynamic Auctions with Low Regret Buyers
On Single Source Robustness in Deep Fusion Models
Policy Evaluation with Latent Confounders via Optimal Balance
Cost Effective Active Search
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs
A Stratified Approach to Robustness for Randomly Smoothed Classifiers
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Learning Mean-Field Games
Deep imitation learning for molecular inverse problems
Visual Concept-Metaconcept Learning
Neural Similarity Learning
Ordered Memory
Fast Parallel Algorithms for Statistical Subset Selection Problems
Mutually Regressive Point Processes
Data-driven Estimation of Sinusoid Frequencies
ANODEV2: A Coupled Neural ODE Framework
On the Utility of Learning about Humans for Human-AI Coordination
Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
On Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
On the Accuracy of Influence Functions for Measuring Group Effects
On Testing for Biases in Peer Review
Balancing Efficiency and Fairness in On-Demand Ridesourcing
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion
Adaptive Sequence Submodularity
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
Optimal Stochastic and Online Learning with Individual Iterates
Policy Learning for Fairness in Ranking
Off-Policy Evaluation of Generalization for Deep Q-Learning in Binary Reward Tasks
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Adaptive Influence Maximization with Myopic Feedback
Limiting Extrapolation in Linear Approximate Value Iteration
Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently
In-Place Near Zero-Cost Memory Protection for DNN
Mixtape: Breaking the Softmax Bottleneck Efficiently
Variance Reduced Policy Evaluation with Smooth Function Approximation
Abstract Reasoning with Distracting Features
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer
Doubly-Robust Lasso Bandit
Learning by Abstraction: The Neural State Machine for Visual Reasoning
Equipping Experts/Bandits with Long-term Memory
Scalable inference of topic evolution via models for latent geometric structures
Efficiently escaping saddle points on manifolds
Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces
Towards a Zero-One Law for Column Subset Selection
Deep Model Transferability from Attribution Maps
Anti-efficient encoding in emergent communication
Singleshot : a scalable Tucker tensor decomposition
Neural Machine Translation with Soft Prototype
On the Statistical Properties of Multilabel Learning
Optimal Sparsity-Sensitive Bounds for  Distributed Mean Estimation
Single-Model Uncertainties for Deep Learning
Is Deeper Better only when Shallow is Good?
Domain Generalization via Model-Agnostic Learning of Semantic Features
First-order methods almost always avoid saddle points: The case of Vanishing step-sizes
Oblivious Sampling Algorithms for Private Data Analysis
Nonstochastic Multiarmed Bandits with Unrestricted Delays
Efficient Pure Exploration in Adaptive Round model
Fast AutoAugment
Learning Nonsymmetric Determinantal Point Processes
Hypothesis Set Stability and Generalization
Precision-Recall Balanced Topic Modelling
Discriminative Topic Modeling with Logistic LDA
Discriminator optimal transport
Are Anchor Points Really Indispensable in Label-Noise Learning?
Differentiable Sorting using Optimal Transport: The Sinkhorn CDF and Quantile Operator
Likelihood-Free Overcomplete ICA and ApplicationsIn Causal Discovery
Interior-point Methods Strike Back: Solving the Wasserstein Barycenter Problem
Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
On the convergence of single-call stochastic extra-gradient methods
Infra-slow brain dynamics as a marker for cognitive function and decline
Robust Principle Component Analysis with Adaptive Neighbors
The bias of the sample mean in multi-armed bandits can be positive or negative
First Order Motion Model for Image Animation
Efficient and Thrifty Voting by Any Means Necessary
Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning
Coresets for Archetypal Analysis
Escaping from saddle points on Riemannian manifolds
Localized Structured Prediction
Normalization Helps Training of Quantized LSTM
Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration
Deep Scale-spaces: Equivariance Over Scale
Implicit Regularization in Deep Matrix Factorization
Minimizers of the Empirical Risk and Risk Monotonicity
Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints
Nonlinear scaling of resource allocation in sensory bottlenecks
MAVEN: Multi-Agent Variational Exploration
Competitive Gradient Descent
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
The Parameterized Complexity of Cascading Portfolio Scheduling
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families
The Implicit Bias of AdaGrad on Separable Data
Re-examination of the Role of Latent Variables in Sequence Modeling
Stein Variational Gradient Descent With Matrix-Valued Kernels
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
A Model to Search for Synthesizable Molecules
Differentially Private Anonymized Histograms
Dynamic Local Regret for Non-convex Online Forecasting
Learning Local Search Heuristics for Boolean Satisfiability
Provably Efficient Q-Learning with Low Switching Cost
PyTorch: An Imperative Style, High-Performance Deep Learning Library
A Debiased MDI Feature Importance Measure for Random Forests
Difference Maximization Q-learning: Provably Efficient Q-learning with Function Approximation
Rapid Convergence of the Unadjusted Langevin Algorithm: Log-Sobolev Suffices
Large-scale optimal transport map estimation using projection pursuit
Fast Decomposable Submodular Function Minimization using Constrained Total Variation
Which Algorithmic Choices Matter at Which Batch Sizes?  Insights From a Noisy Quadratic Model
Average Individual Fairness: Algorithms, Generalization and Experiments
Manipulating a Learning Defender and Ways to Counteract
Fast and Accurate Least-Mean-Squares Solvers
Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay 
Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost
Robust and Communication-Efficient Collaborative Learning
Multiclass Learning from Contradictions
Learning from Trajectories via Subgoal Discovery
An adaptive Mirror-Prox method for variational inequalities with singular operators
Facility Location Problem in Differential Privacy Model Revisited 
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator
A Universally Optimal Multistage Accelerated Stochastic Gradient Method
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
Large Memory Layers with Product Keys
Learning Deterministic Weighted Automata with Queries and Counterexamples
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals
Visualizing and Measuring the Geometry of BERT
Self-Critical Reasoning for Robust Visual Question Answering
Learning to Screen
A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers
A Little Is Enough: Circumventing Defenses For Distributed Learning
Finite-Sample Analysis for SARSA with Linear Function Approximation
Private Learning Implies Online Learning: An Efficient Reduction
The Functional Neural Process
Unlocking Fairness: a Trade-off Revisited
Fisher Efficient Inference of Intractable Models
Thompson Sampling and Approximate Inference
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization
Approximating the Permanent by Sampling from Adaptive Partitions
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration
Online Learning via the Differential Privacy Lens
Parameter elimination in particle Gibbs sampling
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Neural Multisensory Scene Inference
Regret Bounds for Thompson Sampling in Restless Bandit Problems
Better Transfer Learning Through Inferred Successor Maps
Defending Against Neural Fake News
Sample Adaptive MCMC
A Stochastic Composite Gradient Method with Incremental Variance Reduction
Write, Execute, Assess: Program Synthesis with a REPL
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback
Linear Stochastic Bandits Under Safety Constraints
A coupled autoencoder approach for multi-modal analysis of cell types
The Discovery of Useful Questions as Auxiliary Tasks
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias
Multiclass Performance Metric Elicitation
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
Explicit Explore-Exploit Algorithms in Continuous State Spaces
ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels
Sobolev Independence Criterion 
Learning from brains how to regularize machines
Random Tessellation Forests
Lookahead Optimizer: k steps forward, 1 step back
Understanding the Role of Momentum in Stochastic Gradient Methods
Guided Meta-Policy Search
Evaluating Protein Transfer Learning with TAPE
Systematic generalization through meta sequence-to-sequence learning
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models
Neural Jump Stochastic Differential Equations
ON THE VALUE OF TARGET SAMPLING IN COVARIATE-SHIFT
Meta Learning with Relational Information for Short Sequences
Learning to Learn By Self-Critique
Dying Experts: Efficient Algorithms with Optimal Regret Bounds
Model similarity mitigates test set overuse
A unified theory for the origin of grid cells through the lens of pattern formation
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons
Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond
A Game Theoretic Approach to Class-wise Selective Rationalization
Efficiently avoiding saddle points with zero order methods: No gradients required 
Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization
Average Case Column Subset Selection for Entrywise $\ell_1$-Norm Loss
Learning Mixtures of Plackett-Luce Models from Structured Partial Orders
Certainty Equivalence is Efficient for Linear Quadratic Control
Logarithmic Regret for Online Control
Elliptical Perturbations for Differential Privacy
KNG: The K-Norm Gradient Mechanism
Sequential Neural Processes
Policy Continuation with Hindsight Inverse Dynamics
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation
Limits of Private Learning with Access to Public Data
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards
Statistical-Computational Tradeoff in Single Index Models
On Fenchel Mini-Max Learning
Poincar\'{e} Recurrence, Cycles and Spurious Equilibria in Gradient Descent for Non-Convex Non-Concave Zero-Sum Games
Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin
Efficient Convex Relaxations for Streaming PCA
G2SAT: Learning to Generate SAT Formulas
Dimensionality reduction: theoretical perspective on practical measures
Multilabel reductions: what is my loss optimising?
Deep Gamblers: Learning to Abstain with Portfolio Theory
Sequential Experimental Design for Transductive Linear Bandits
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering
Optimizing Generalized Rate Metrics through Three-player Games
Program Synthesis and Semantic Parsing with Learned Code Idioms
Random Projections with Asymmetric Quantization
Superposition of many models into one
On Making Stochastic Classifiers Deterministic
Statistical Model Aggregation via Parameter Matching
The Broad Optimality of Profile Maximum Likelihood
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates
On Distributed Averaging for Stochastic k-PCA
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
Online Forecasting of Total-Variation-bounded Sequences
Local SGD with  Periodic Averaging: Tighter Analysis  and Adaptive Synchronization 
Dynamic Curriculum Learning by Gradient Descent
Unified Sample-Optimal Property Estimation in Near-Linear Time
Learning Stable Deep Dynamics Models
Image Captioning: Transforming Objects into Words
On Tractable Computation of Expected Predictions
Levenshtein Transformer
Machine Teaching of Active Sequential Learners
Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test
Demystifying Black-box Models with Symbolic Metamodels
Neural Temporal-Difference Learning Converges to Global Optima
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces
Attentive State-Space Modeling of Disease Progression
A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport
Variance Reduction for Matrix Games
Distributed estimation of the inverse Hessian by determinantal averaging
Smoothing Structured Decomposable Circuits
Provable Non-linear Inductive Matrix Completion
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback
A Necessary and Sufficient Stability Notion for Adaptive Generalization
Necessary and Sufficient Geometries for Adaptive Gradient Algorithms
The Thermodynamic Variational Objective
Exact sampling of determinantal point processes with sublinear time preprocessing
Geometry-Aware Neural Rendering
Variational Temporal Abstraction
Learning Auctions with Robust Incentive Guarantees
Uniform convergence may be unable to explain generalization in deep learning
Thresholding Bandit with Optimal Aggregate Regret
Causal Misidentification in Imitation Learning
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data
Dimension-Free Bounds for Low-Precision Training
Concentration of risk measures: A Wasserstein distance approach
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Gradient based sample selection for online continual learning 
Online Continual Learning with Maximal Interfered Retrieval
Neural Attribution for Semantic Bug-Localization in Student Programs
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
SPoC: Search-based Pseudocode to Code
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
Margin-Based Generalization Lower Bounds for Boosted Classifiers
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio
Weighted Linear Bandits for Non-Stationary Environments
Pareto Multi-Task Learning
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits
Submodular Function Minimization with Noisy Evaluation Oracle
Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach
Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling
Bootstrapping Upper Confidence Bound
Structured Prediction with Projection Oracles
Primal Dual Formulation For Deep Learning With Constraints
Screening Sinkhorn Algorithm for Regularized Optimal Transport
Multiagent Evaluation under Incomplete Information
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration
Fast and Accurate Stochastic Gradient Estimation
Root Mean Square Layer Normalization
Universality in Learning from Linear Measurements
Exponentially convergent stochastic k-PCA without variance reduction
A General Framework for Efficient Symmetric Property Estimation
Structured Variational Inference in Continuous Cox Process Models
Hindsight Credit Assignment
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets
Control What You Can: Intrinsically Motivated Task-Planning Agent
Selecting causal brain features with a single conditional independence test per feature
Beating SGD Saturation with Tail-Averaging  and Minibatching
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond
Curriculum-guided Hindsight Experience Replay
MetaInit: Initializing learning by learning to initialize
Random Path Selection for Continual Learning
Causal Regularization
Learning Hawkes Processes from a handful of events
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
Uncertainty on Asynchronous Event Prediction
Accurate, reliable and fast robustness evaluation
Triad Constraints for Learning Causal Structure of Latent Variables
On the Inductive Bias of Neural Tangent Kernels
Cross-Domain Transferable Perturbations
Kernel quadrature with DPPs
REM: From Structural Entropy to Community Structure Deception 
Minimum Stein Discrepancy Estimators
Fast and Furious Learning in Zero-Sum Games: Vanishing Regret with Non-Vanishing Step Sizes
Learning to Correlate in Multi-Player General-Sum Sequential Games
Stochastic Continuous Greedy ++:  When Upper and Lower Bounds Match
Learning step sizes for unfolded sparse coding
Communication-efficient Distributed SGD with Sketching
Modeling Conceptual Understanding in Image Reference Games
Near Neighbor: Who is the Fairest of Them All?
Outlier-robust estimation of a sparse linear model using $\ell_1$-penalized Huber's $M$-estimator
Learning nonlinear level sets for dimensionality reduction in function approximation
Online Convex Matrix Factorization with Representative Regions
A Fourier Perspective on Model Robustness in Computer Vision
Privacy Amplification by Mixing and Diffusion Mechanisms
Metalearned Neural Memory
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data
Cold Case: The Lost MNIST Digits
Globally Optimal Learning for Structured Elliptical Losses
RUDDER: Return Decomposition for Delayed Rewards
Communication trade-offs for synchronized distributed SGD with large step size
No-Regret Learning in Unknown Games with Correlated Payoffs
Alleviating Label Switching with Optimal Transport
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Compacting, Picking and Growing for Unforgetting Continual Learning
Approximating Interactive Human Evaluation withSelf-Play for Open-Domain Dialog Systems
Towards Hardware-Aware Tractable Learning of Probabilistic Models
Rand-NSG: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node
Sampled softmax with random Fourier features
Learning Fairness in Multi-Agent Systems
Primal-Dual Block Frank-Wolfe
The Implicit Metropolis-Hastings Algorithm
Can you trust your model's uncertainty?  Evaluating predictive uncertainty under dataset shift
A First-Order Approach to Accelerated Value Iteration
Are Sixteen Heads Really Better than One?
User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning
Recovering Bandits
Learning Positive Functions with Pseudo Mirror Descent
Differentially Private Covariance Estimation
Integrating mechanistic and structural causal models enables counterfactual inference in complex systems
Stochastic Frank-Wolfe for Composite Convex Minimization
Consistent Constraint-Based Causal Structure Learning 
Robust Attribution Regularization
Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization
Structure Learning with Side Information: Sample Complexity
Flexible information routing in neural populations through stochastic comodulation
Generalization Bounds in the Predict-then-Optimize Framework
Categorized Bandits
Worst-Case Regret Bounds for Exploration via Randomized Value Functions
Efficient characterization of electrically evoked responses for neural interfaces
Differentially Private Distributed Data Summarization under Covariate Shift
Hamiltonian descent for composite objectives
Implicit Regularization of Accelerated Methods in Hilbert Spaces
Non-Asymptotic Pure Exploration by Solving Games
Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback
Reflection Separation using a Pair of Unpolarized and Polarized Images
Pure Exploration with Multiple Correct Answers
Compiler Auto-Vectorization using Imitation Learning
A Generalized Algorithm for Multi-Objective RL and Policy Adaptation
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations
Sliced Gromov-Wasserstein
Towards Practical Alternating Least-Squares for CCA
Deep Leakage from Gradients
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
Value Function in Frequency Domain and Characteristic Value Iteration
Icebreaker: Efficient Information Acquisition with Active Learning
Planning with Goal-Conditioned Policies
Don't take it lightly: Phasing optical random projections with unknown operators
Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis
Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods
Compositional Plan Vectors
Locally Private Learning without Interaction Requires Separation
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences
Population-based Meta-Optimizer Guided by Posterior Estimation
On Human-Aligned Risk Minimization
Semi-Parametric Efficient Policy Learning with Continuous Actions
Precise and Scalable Convex Relaxations for Robustness Certification
Efficiently Learning Fourier Sparse Set Functions
Multi-Criteria Dimensionality Reduction with Applications to Fairness
Fast Agent Resetting in Training
Heterogeneous Treatment Effects with Instruments
Understanding Sparse JL for Feature Hashing
Momentum-Based Variance Reduction in Non-Convex SGD
Faster width-dependent algorithm for mixed packing and covering LPs
Goal-conditioned Imitation Learning
Multiple Futures Prediction
A Perspective on False Discovery Rate Control via Knockoffs
A Kernel Loss for Solving the Bellman Equation
Differential Privacy Has Disparate Impact on Model Accuracy
Learning Deep MRFs with Amortized Bethe Free Energy Minimization
Fast structure learning with modular regularization
TAB-VCR: Tags and Attributes for Visual Commonsense Reasoning
Improving Model Robustness and Uncertainty Estimates with Self-Supervised Learning
On the Role of Inductive Bias From Simulation and the Transfer to the Real World: a new Disentanglement Dataset
Learning Data Manipulation for Augmentation and Weighting
Space and Time Efficient Kernel Density Estimation in High Dimensions
Generalization in multitask deep neural classifiers: a statistical physics approach
Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling