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【论文阅读笔记】NeurIPS2020文章列表Part2

2023-05-16
  • Online Multitask Learning with Long-Term Memory
  • Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies
  • Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
  • On Reward-Free Reinforcement Learning with Linear Function Approximation
  • Robustness of Community Detection to Random Geometric Perturbations
  • Learning outside the Black-Box: The pursuit of interpretable models
  • Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization
  • Robust large-margin learning in hyperbolic space
  • Replica-Exchange Nos’e-Hoover Dynamics for Bayesian Learning on Large Datasets
  • Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
  • Neural Anisotropy Directions
  • Digraph Inception Convolutional Networks
  • PAC-Bayesian Bound for the Conditional Value at Risk
  • Stochastic Stein Discrepancies
  • On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
  • Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search
  • Fair Multiple Decision Making Through Soft Interventions
  • Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatment
  • Learning to Play No-Press Diplomacy with Best Response Policy Iteration
  • Inverse Learning of Symmetries
  • DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling
  • Distributed Newton Can Communicate Less and Resist Byzantine Workers
  • Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
  • Effective Diversity in Population Based Reinforcement Learning
  • Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data
  • Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
  • Hybrid Models for Learning to Branch
  • WoodFisher: Efficient Second-Order Approximation for Neural Network Compression
  • Bi-level Score Matching for Learning Energy-based Latent Variable Models
  • Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding
  • Decision trees as partitioning machines to characterize their generalization properties
  • Learning to Prove Theorems by Learning to Generate Theorems
  • 3D Self-Supervised Methods for Medical Imaging
  • Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods
  • Worst-Case Analysis for Randomly Collected Data
  • Truthful Data Acquisition via Peer Prediction
  • Learning Robust Decision Policies from Observational Data
  • Byzantine Resilient Distributed Multi-Task Learning
  • Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting
  • Improving model calibration with accuracy versus uncertainty optimization
  • The Convolution Exponential and Generalized Sylvester Flows
  • An Improved Analysis of Stochastic Gradient Descent with Momentum
  • Precise expressions for random projections: Low-rank approximation and randomized Newton
  • The MAGICAL Benchmark for Robust Imitation
  • X-CAL: Explicit Calibration for Survival Analysis
  • Decentralized Accelerated Proximal Gradient Descent
  • Making Non-Stochastic Control (Almost) as Easy as Stochastic
  • BERT Loses Patience: Fast and Robust Inference with Early Exit
  • Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization
  • BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning
  • Regularizing Towards Permutation Invariance In Recurrent Models
  • What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes
  • Batch normalization provably avoids ranks collapse for randomly initialised deep networks
  • Choice Bandits
  • What if Neural Networks had SVDs?
  • A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices
  • CoMIR: Contrastive Multimodal Image Representation for Registration
  • Ensuring Fairness Beyond the Training Data
  • How do fair decisions fare in long-term qualification?
  • Pre-training via Paraphrasing
  • GCN meets GPU: Decoupling “When to Sample” from “How to Sample”
  • Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks
  • All your loss are belong to Bayes
  • HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
  • Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
  • Non-Convex SGD Learns Halfspaces with Adversarial Label Noise
  • A Tight Lower Bound and Efficient Reduction for Swap Regret
  • DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction
  • OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling
  • Measuring Robustness to Natural Distribution Shifts in Image Classification
  • Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference
  • RandAugment: Practical Automated Data Augmentation with a Reduced Search Space
  • Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model
  • DisARM: An Antithetic Gradient Estimator for Binary Latent Variables
  • Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
  • Supervised Contrastive Learning
  • Learning Optimal Representations with the Decodable Information Bottleneck
  • Meta-trained agents implement Bayes-optimal agents
  • Learning Agent Representations for Ice Hockey
  • Weak Form Generalized Hamiltonian Learning
  • Neural Non-Rigid Tracking
  • Collegial Ensembles
  • ICNet: Intra-saliency Correlation Network for Co-Saliency Detection
  • Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows
  • Deep Metric Learning with Spherical Embedding
  • Preference-based Reinforcement Learning with Finite-Time Guarantees
  • AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
  • Interpretable Sequence Learning for Covid-19 Forecasting
  • Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
  • Modern Hopfield Networks and Attention for Immune Repertoire Classification
  • One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers
  • Task-Robust Model-Agnostic Meta-Learning
  • R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making
  • Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity
  • Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev
  • Tensor Completion Made Practical
  • Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
  • Content Provider Dynamics and Coordination in Recommendation Ecosystems
  • Almost Surely Stable Deep Dynamics
  • Experimental design for MRI by greedy policy search
  • Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
  • ColdGANs: Taming Language GANs with Cautious Sampling Strategies
  • Hedging in games: Faster convergence of external and swap regrets
  • The Origins and Prevalence of Texture Bias in Convolutional Neural Networks
  • Time-Reversal Symmetric ODE Network
  • Provable Overlapping Community Detection in Weighted Graphs
  • Fast Unbalanced Optimal Transport on a Tree
  • Acceleration with a Ball Optimization Oracle
  • Avoiding Side Effects By Considering Future Tasks
  • Handling Missing Data with Graph Representation Learning
  • Improving Auto-Augment via Augmentation-Wise Weight Sharing
  • MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles
  • HRN: A Holistic Approach to One Class Learning
  • The Generalized Lasso with Nonlinear Observations and Generative Priors
  • Fair regression via plug-in estimator and recalibration with statistical guarantees
  • Modeling Shared responses in Neuroimaging Studies through MultiView ICA
  • Efficient Planning in Large MDPs with Weak Linear Function Approximation
  • Efficient Learning of Generative Models via Finite-Difference Score Matching
  • Semialgebraic Optimization for Lipschitz Constants of ReLU Networks
  • Linear-Sample Learning of Low-Rank Distributions
  • Transferable Calibration with Lower Bias and Variance in Domain Adaptation
  • Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics
  • Online Bayesian Goal Inference for Boundedly Rational Planning Agents
  • BayReL: Bayesian Relational Learning for Multi-omics Data Integration
  • Weakly Supervised Deep Functional Maps for Shape Matching
  • Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
  • Rethinking the Value of Labels for Improving Class-Imbalanced Learning
  • Provably Robust Metric Learning
  • Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
  • COPT: Coordinated Optimal Transport on Graphs
  • No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
  • Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets
  • Self-Adaptive Training: beyond Empirical Risk Minimization
  • Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization
  • Near-Optimal Comparison Based Clustering
  • Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement
  • A new convergent variant of Q-learning with linear function approximation
  • TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation
  • Neural Networks with Small Weights and Depth-Separation Barriers
  • Untangling tradeoffs between recurrence and self-attention in artificial neural networks
  • Dual-Free Stochastic Decentralized Optimization with Variance Reduction
  • Online Learning in Contextual Bandits using Gated Linear Networks
  • Throughput-Optimal Topology Design for Cross-Silo Federated Learning
  • Quantized Variational Inference
  • Asymptotically Optimal Exact Minibatch Metropolis-Hastings
  • Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search
  • Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests
  • Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks
  • Space-Time Correspondence as a Contrastive Random Walk
  • The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space
  • Exponential ergodicity of mirror-Langevin diffusions
  • An Efficient Framework for Clustered Federated Learning
  • Autoencoders that don’t overfit towards the Identity
  • Polynomial-Time Computation of Optimal Correlated Equilibria in Two-Player Extensive-Form Games with Public Chance Moves and Beyond
  • Parameterized Explainer for Graph Neural Network
  • Recursive Inference for Variational Autoencoders
  • Flexible mean field variational inference using mixtures of non-overlapping exponential families
  • HYDRA: Pruning Adversarially Robust Neural Networks
  • NVAE: A Deep Hierarchical Variational Autoencoder
  • Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
  • What Do Neural Networks Learn When Trained With Random Labels?
  • Counterfactual Prediction for Bundle Treatment
  • Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
  • Learning Disentangled Representations and Group Structure of Dynamical Environments
  • Learning Linear Programs from Optimal Decisions
  • Wisdom of the Ensemble: Improving Consistency of Deep Learning Models
  • Universal Function Approximation on Graphs
  • Accelerating Reinforcement Learning through GPU Atari Emulation
  • EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
  • Comparator-Adaptive Convex Bandits
  • Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs
  • The Adaptive Complexity of Maximizing a Gross Substitutes Valuation
  • A Robust Functional EM Algorithm for Incomplete Panel Count Data
  • Graph Stochastic Neural Networks for Semi-supervised Learning
  • Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition
  • A Benchmark for Systematic Generalization in Grounded Language Understanding
  • Weston-Watkins Hinge Loss and Ordered Partitions
  • Reinforcement Learning with Augmented Data
  • Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes
  • Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning
  • Estimating Training Data Influence by Tracing Gradient Descent
  • Joint Policy Search for Multi-agent Collaboration with Imperfect Information
  • Adversarial Bandits with Corruptions: Regret Lower Bound and No-regret Algorithm
  • Beta R-CNN: Looking into Pedestrian Detection from Another Perspective
  • Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks
  • Learning Retrospective Knowledge with Reverse Reinforcement Learning
  • Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data
  • GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs
  • A General Large Neighborhood Search Framework for Solving Integer Linear Programs
  • A Theoretical Framework for Target Propagation
  • OrganITE: Optimal transplant donor organ offering using an individual treatment effect
  • The Complete Lasso Tradeoff Diagram
  • On the universality of deep learning
  • Regression with reject option and application to kNN
  • The Primal-Dual method for Learning Augmented Algorithms
  • FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
  • A Class of Algorithms for General Instrumental Variable Models
  • Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
  • Bayesian Optimization of Risk Measures
  • TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search
  • GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
  • PIE-NET: Parametric Inference of Point Cloud Edges
  • A Simple Language Model for Task-Oriented Dialogue
  • A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval
  • Confidence sequences for sampling without replacement
  • A mean-field analysis of two-player zero-sum games
  • Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge
  • Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games
  • Improving Sparse Vector Technique with Renyi Differential Privacy
  • Latent Template Induction with Gumbel-CRFs
  • Instance Based Approximations to Profile Maximum Likelihood
  • Factorizable Graph Convolutional Networks
  • Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
  • A Study on Encodings for Neural Architecture Search
  • Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
  • Early-Learning Regularization Prevents Memorization of Noisy Labels
  • LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond
  • Learning Parities with Neural Networks
  • Consistent Plug-in Classifiers for Complex Objectives and Constraints
  • Movement Pruning: Adaptive Sparsity by Fine-Tuning
  • Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot
  • Online Matrix Completion with Side Information
  • Position-based Scaled Gradient for Model Quantization and Pruning
  • Online Learning with Primary and Secondary Losses
  • Graph Information Bottleneck
  • The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise
  • Adaptive Online Estimation of Piecewise Polynomial Trends
  • RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
  • Agnostic Learning with Multiple Objectives
  • 3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data
  • Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation
  • Differentiable Top-k with Optimal Transport
  • Information-theoretic Task Selection for Meta-Reinforcement Learning
  • A Limitation of the PAC-Bayes Framework
  • On Completeness-aware Concept-Based Explanations in Deep Neural Networks
  • Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems
  • Why Normalizing Flows Fail to Detect Out-of-Distribution Data
  • Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay
  • Unsupervised Translation of Programming Languages
  • Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
  • Optimally Deceiving a Learning Leader in Stackelberg Games
  • Online Optimization with Memory and Competitive Control
  • IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
  • Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation
  • Learning from Failure: De-biasing Classifier from Biased Classifier
  • Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder
  • Deep Diffusion-Invariant Wasserstein Distributional Classification
  • Finding All ϵ \epsilon ϵ-Good Arms in Stochastic Bandits
  • Meta-Learning through Hebbian Plasticity in Random Networks
  • A Computational Separation between Private Learning and Online Learning
  • Top-KAST: Top-K Always Sparse Training
  • Meta-Learning with Adaptive Hyperparameters
  • Tight last-iterate convergence rates for no-regret learning in multi-player games
  • Curvature Regularization to Prevent Distortion in Graph Embedding
  • Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability
  • Statistical and Topological Properties of Sliced Probability Divergences
  • Probabilistic Active Meta-Learning
  • Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher
  • Adversarial Attacks on Deep Graph Matching
  • The Generalization-Stability Tradeoff In Neural Network Pruning
  • Gradient-EM Bayesian Meta-Learning
  • Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems
  • Linearly Converging Error Compensated SGD
  • Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction
  • A Self-Tuning Actor-Critic Algorithm
  • The Cone of Silence: Speech Separation by Localization
  • High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds
  • Train-by-Reconnect: Decoupling Locations of Weights from Their Values
  • Learning discrete distributions: user vs item-level privacy
  • Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula
  • Sparse and Continuous Attention Mechanisms
  • Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
  • Learning by Minimizing the Sum of Ranked Range
  • Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations
  • Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
  • Fair Hierarchical Clustering
  • Self-training Avoids Using Spurious Features Under Domain Shift
  • Improving Online Rent-or-Buy Algorithms with Sequential Decision Making and ML Predictions
  • CircleGAN: Generative Adversarial Learning across Spherical Circles
  • WOR and p p p's: Sketches for ℓ p \ell_p ℓp​-Sampling Without Replacement
  • Hypersolvers: Toward Fast Continuous-Depth Models
  • Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
  • Escaping the Gravitational Pull of Softmax
  • Regret in Online Recommendation Systems
  • On Convergence and Generalization of Dropout Training
  • Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking
  • Implicit Regularization in Deep Learning May Not Be Explainable by Norms
  • POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
  • Uncertainty-aware Self-training for Few-shot Text Classification
  • Learning to Learn with Feedback and Local Plasticity
  • Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization
  • Sharper Generalization Bounds for Pairwise Learning
  • A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings
  • Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality
  • Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning
  • Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning
  • RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
  • Efficient Clustering for Stretched Mixtures: Landscape and Optimality
  • A Group-Theoretic Framework for Data Augmentation
  • The Statistical Cost of Robust Kernel Hyperparameter Turning
  • How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks?
  • ContraGAN: Contrastive Learning for Conditional Image Generation
  • On the distance between two neural networks and the stability of learning
  • A Topological Filter for Learning with Label Noise
  • Personalized Federated Learning with Moreau Envelopes
  • Avoiding Side Effects in Complex Environments
  • No-regret Learning in Price Competitions under Consumer Reference Effects
  • Geometric Dataset Distances via Optimal Transport
  • Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters
  • A novel variational form of the Schatten- p p p quasi-norm
  • Energy-based Out-of-distribution Detection
  • On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
  • User-Dependent Neural Sequence Models for Continuous-Time Event Data
  • Active Structure Learning of Causal DAGs via Directed Clique Trees
  • Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
  • BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
  • Reconsidering Generative Objectives For Counterfactual Reasoning
  • Robust Federated Learning: The Case of Affine Distribution Shifts
  • Quantile Propagation for Wasserstein-Approximate Gaussian Processes
  • Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning
  • High-contrast “gaudy” images improve the training of deep neural network models of visual cortex
  • Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion
  • Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms
  • H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks
  • Neural Unsigned Distance Fields for Implicit Function Learning
  • Curriculum By Smoothing
  • Fast Transformers with Clustered Attention
  • The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification
  • Strongly Incremental Constituency Parsing with Graph Neural Networks
  • AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection
  • Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation
  • Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians
  • First-Order Methods for Large-Scale Market Equilibrium Computation
  • Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects
  • Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis
  • A General Method for Robust Learning from Batches
  • Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning
  • Hard Negative Mixing for Contrastive Learning
  • MOReL: Model-Based Offline Reinforcement Learning
  • Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
  • Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion
  • Learning Semantic-aware Normalization for Generative Adversarial Networks
  • Differentiable Causal Discovery from Interventional Data
  • One-sample Guided Object Representation Disassembling
  • Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate
  • Robust Persistence Diagrams using Reproducing Kernels
  • Contextual Games: Multi-Agent Learning with Side Information
  • Goal-directed Generation of Discrete Structures with Conditional Generative Models
  • Beyond Lazy Training for Over-parameterized Tensor Decomposition
  • Denoised Smoothing: A Provable Defense for Pretrained Classifiers
  • Minibatch Stochastic Approximate Proximal Point Methods
  • Attribute Prototype Network for Zero-Shot Learning
  • CrossTransformers: spatially-aware few-shot transfer
  • Learning Latent Space Energy-Based Prior Model
  • SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology
  • Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation
  • High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization
  • Model Fusion via Optimal Transport
  • On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems
  • Learning Individually Inferred Communication for Multi-Agent Cooperation
  • Set2Graph: Learning Graphs From Sets
  • Graph Random Neural Networks for Semi-Supervised Learning on Graphs
  • Gradient Boosted Normalizing Flows
  • Open Graph Benchmark: Datasets for Machine Learning on Graphs
  • Towards Understanding Hierarchical Learning: Benefits of Neural Representations
  • Texture Interpolation for Probing Visual Perception
  • Hierarchical Neural Architecture Search for Deep Stereo Matching
  • MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models
  • Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy
  • Focus of Attention Improves Information Transfer in Visual Features
  • Auditing Differentially Private Machine Learning: How Private is Private SGD?
  • A Dynamical Central Limit Theorem for Shallow Neural Networks
  • Measuring Systematic Generalization in Neural Proof Generation with Transformers
  • Big Self-Supervised Models are Strong Semi-Supervised Learners
  • Learning from Label Proportions: A Mutual Contamination Framework
  • Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
  • Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs
  • Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning
  • Model Class Reliance for Random Forests
  • Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games
  • Agnostic Q Q Q-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity
  • Learning to Adapt to Evolving Domains
  • Synthesizing Tasks for Block-based Programming
  • Scalable Belief Propagation via Relaxed Scheduling
  • Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks
  • Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret
  • Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes
  • Faster DBSCAN via subsampled similarity queries
  • De-Anonymizing Text by Fingerprinting Language Generation
  • Multiparameter Persistence Image for Topological Machine Learning
  • PLANS: Neuro-Symbolic Program Learning from Videos
  • Matrix Inference and Estimation in Multi-Layer Models
  • MeshSDF: Differentiable Iso-Surface Extraction
  • Variational Interaction Information Maximization for Cross-domain Disentanglement
  • Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning
  • Faithful Embeddings for Knowledge Base Queries
  • Wasserstein Distances for Stereo Disparity Estimation
  • Multi-agent Trajectory Prediction with Fuzzy Query Attention
  • Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping
  • An Analysis of SVD for Deep Rotation Estimation
  • Can the Brain Do Backpropagation? — Exact Implementation of Backpropagation in Predictive Coding Networks
  • Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
  • Distributed Distillation for On-Device Learning
  • COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
  • Passport-aware Normalization for Deep Model Protection
  • Sampling-Decomposable Generative Adversarial Recommender
  • Limits to Depth Efficiencies of Self-Attention
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