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Bootstrapping the Error of Oja's Algorithm
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Uniform Sampling over Episode Difficulty
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Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima
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NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
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Optimal Sketching for Trace Estimation
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Foundations of Symbolic Languages for Model Interpretability
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Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval
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Property-Aware Relation Networks for Few-Shot Molecular Property Prediction
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Second-Order Neural ODE Optimizer
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Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities
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Learning to See by Looking at Noise
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IQ-Learn: Inverse soft-Q Learning for Imitation
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Towards a Unified Information-Theoretic Framework for Generalization
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Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update
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A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition
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Uncertain Decisions Facilitate Better Preference Learning
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DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
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Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms
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Robust Learning of Optimal Auctions
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The Semi-Random Satisfaction of Voting Axioms
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Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems
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Double Machine Learning Density Estimation for Local Treatment Effects with Instruments
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Long Short-Term Transformer for Online Action Detection
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Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer
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Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning
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MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge
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A flow-based latent state generative model of neural population responses to natural images
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A Unified Approach to Fair Online Learning via Blackwell Approachability
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A single gradient step finds adversarial examples on random two-layers neural networks
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Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations
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Bayesian Bellman Operators
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PLUGIn: A simple algorithm for inverting generative models with recovery guarantees
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Auditing Black-Box Prediction Models for Data Minimization Compliance
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Private Non-smooth ERM and SCO in Subquadratic Steps
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Fast Bayesian Inference for Gaussian Cox Processes via Path Integral Formulation
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Reliable Estimation of KL Divergence using a Discriminator in Reproducing Kernel Hilbert Space
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Image Generation using Continuous Filter Atoms
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ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning
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Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience
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Learning to Compose Visual Relations
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Dense Keypoints via Multiview Supervision
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Look at What I’m Doing: Self-Supervised Spatial Grounding of Narrations in Instructional Videos
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Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering
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Mixture Proportion Estimation and PU Learning:A Modern Approach
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Refining Language Models with Compositional Explanations
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Rethinking gradient sparsification as total error minimization
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Efficient Online Estimation of Causal Effects by Deciding What to Observe
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Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality
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Safe Reinforcement Learning with Natural Language Constraints
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Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning
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Speedy Performance Estimation for Neural Architecture Search
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On Inductive Biases for Heterogeneous Treatment Effect Estimation
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Task-Adaptive Neural Network Search with Meta-Contrastive Learning
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Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency
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Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions
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Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret
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Pruning Randomly Initialized Neural Networks with Iterative Randomization
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Mixability made efficient: Fast online multiclass logistic regression
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Credit Assignment in Neural Networks through Deep Feedback Control
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Probabilistic Tensor Decomposition of Neural Population Spiking Activity
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Batch Normalization Orthogonalizes Representations in Deep Random Networks
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H-NeRF: Neural Radiance Fields for Rendering and Temporal Reconstruction of Humans in Motion
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Slice Sampling Reparameterization Gradients
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Techniques for Symbol Grounding with SATNet
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On the Representation of Solutions to Elliptic PDEs in Barron Spaces
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Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate
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ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction
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Lossy Compression for Lossless Prediction
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PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair
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Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution
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Repulsive Deep Ensembles are Bayesian
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Deep Self-Dissimilarities as Powerful Visual Fingerprints
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What’s a good imputation to predict with missing values?
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Breaking the Dilemma of Medical Image-to-image Translation
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TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation
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Subgroup Generalization and Fairness of Graph Neural Networks
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Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems
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Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation
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Shaping embodied agent behavior with activity-context priors from egocentric video
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Differentially Private Model Personalization
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Understanding the Under-Coverage Bias in Uncertainty Estimation
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Width-based Lookaheads with Learnt Base Policies and Heuristics Over the Atari-2600 Benchmark
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Offline Reinforcement Learning as One Big Sequence Modeling Problem
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Learning to delegate for large-scale vehicle routing
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Maximum Likelihood Training of Score-Based Diffusion Models
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Efficiently Identifying Task Groupings for Multi-Task Learning
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A Minimalist Approach to Offline Reinforcement Learning
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Tensor Normal Training for Deep Learning Models
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Reinforcement Learning in Newcomblike Environments
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Provably Faster Algorithms for Bilevel Optimization
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PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition
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Probabilistic Attention for Interactive Segmentation
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A Provably Efficient Sample Collection Strategy for Reinforcement Learning
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Lower and Upper Bounds on the Pseudo-Dimension of Tensor Network Models
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Uniform Concentration Bounds toward a Unified Framework for Robust Clustering
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Variational Bayesian Optimistic Sampling
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Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems
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Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis
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Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence
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Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent
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Fair Exploration via Axiomatic Bargaining
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Precise characterization of the prior predictive distribution of deep ReLU networks
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Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD
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Littlestone Classes are Privately Online Learnable
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Adversarial Teacher-Student Representation Learning for Domain Generalization
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Measuring Generalization with Optimal Transport
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A Geometric Analysis of Neural Collapse with Unconstrained Features
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SOFT: Softmax-free Transformer with Linear Complexity
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Learning Large Neighborhood Search Policy for Integer Programming
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Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks
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Neural Additive Models: Interpretable Machine Learning with Neural Nets
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Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
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Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning
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Tractable Regularization of Probabilistic Circuits
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Neural optimal feedback control with local learning rules
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A Normative and Biologically Plausible Algorithm for Independent Component Analysis
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SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression
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Coresets for Clustering with Missing Values
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Your head is there to move you around: Goal-driven models of the primate dorsal pathway
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Sequential Algorithms for Testing Closeness of Distributions
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Optimal Policies Tend To Seek Power
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Online Selective Classification with Limited Feedback
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Across-animal odor decoding by probabilistic manifold alignment
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Coresets for Time Series Clustering
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A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms
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Constrained Robust Submodular Partitioning
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Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits
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A Variational Perspective on Diffusion-Based Generative Models and Score Matching
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On the Existence of The Adversarial Bayes Classifier
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RL for Latent MDPs: Regret Guarantees and a Lower Bound
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On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method
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The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning
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FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
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Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation
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Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices
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A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning
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Refined Learning Bounds for Kernel and Approximate $k$-Means
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Hash Layers For Large Sparse Models
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Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State
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Online and Offline Reinforcement Learning by Planning with a Learned Model
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Exact marginal prior distributions of finite Bayesian neural networks
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Conformal Prediction using Conditional Histograms
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RIM: Reliable Influence-based Active Learning on Graphs
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Node Dependent Local Smoothing for Scalable Graph Learning
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Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach
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Explaining Latent Representations with a Corpus of Examples
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Collaborating with Humans without Human Data
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On the Value of Infinite Gradients in Variational Autoencoder Models
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Sliced Mutual Information: A Scalable Measure of Statistical Dependence
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Learning Generalized Gumbel-max Causal Mechanisms
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Early-stopped neural networks are consistent
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Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks
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Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning
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An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence
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Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization
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Continuous vs. Discrete Optimization of Deep Neural Networks
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Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation
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Program Synthesis Guided Reinforcement Learning for Partially Observed Environments
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Learning Disentangled Behavior Embeddings
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Coresets for Decision Trees of Signals
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Offline RL Without Off-Policy Evaluation
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Learning with Holographic Reduced Representations
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On the Power of Differentiable Learning versus PAC and SQ Learning
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Unintended Selection: Persistent Qualification Rate Disparities and Interventions
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Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem
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Counterfactual Invariance to Spurious Correlations in Text Classification
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Information Directed Sampling for Sparse Linear Bandits
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Embedding Principle of Loss Landscape of Deep Neural Networks
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iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder
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Doubly Robust Thompson Sampling with Linear Payoffs
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DiBS: Differentiable Bayesian Structure Learning
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Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces
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Representation Learning for Event-based Visuomotor Policies
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Self-Supervised Learning Disentangled Group Representation as Feature
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Combiner: Full Attention Transformer with Sparse Computation Cost
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Neural Symplectic Form: Learning Hamiltonian Equations on General Coordinate Systems
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Pragmatic Image Compression for Human-in-the-Loop Decision-Making
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Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets
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Iterative Teaching by Label Synthesis
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Mind the Gap: Assessing Temporal Generalization in Neural Language Models
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Representation Learning Beyond Linear Prediction Functions
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Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness
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Online Active Learning with Surrogate Loss Functions
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Robust Predictable Control
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Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems
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Practical Near Neighbor Search via Group Testing
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Regulating algorithmic filtering on social media
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Multiwavelet-based Operator Learning for Differential Equations
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Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables
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Private learning implies quantum stability
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Interactive Label Cleaning with Example-based Explanations
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Proper Value Equivalence
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Neural Program Generation Modulo Static Analysis
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Subgraph Federated Learning with Missing Neighbor Generation
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Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond
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Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent
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A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval
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Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling
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Subgame solving without common knowledge
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On Linear Stability of SGD and Input-Smoothness of Neural Networks
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Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications
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Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering
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Forster Decomposition and Learning Halfspaces with Noise
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A sampling-based circuit for optimal decision making
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Instance-Dependent Partial Label Learning
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Generic Neural Architecture Search via Regression
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Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent
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Reward is enough for convex MDPs
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Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization
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Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning
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Bootstrap Your Object Detector via Mixed Training
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Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate
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Bias and variance of the Bayesian-mean decoder
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Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning
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Neural Scene Flow Prior
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The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning
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Variational Inference for Continuous-Time Switching Dynamical Systems
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Habitat 2.0: Training Home Assistants to Rearrange their Habitat
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DOCTOR: A Simple Method for Detecting Misclassification Errors
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Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network using SGD and Weight Decay
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Intriguing Properties of Vision Transformers
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Numerical Composition of Differential Privacy
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SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search
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Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space
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Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
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Aligning Pretraining for Detection via Object-Level Contrastive Learning
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Excess Capacity and Backdoor Poisoning
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Calibration and Consistency of Adversarial Surrogate Losses
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Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning
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Decentralized Learning in Online Queuing Systems
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Behavior From the Void: Unsupervised Active Pre-Training
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Towards optimally abstaining from prediction with OOD test examples
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Ultrahyperbolic Neural Networks
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Logarithmic Regret in Feature-based Dynamic Pricing
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Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems
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Learning Equilibria in Matching Markets from Bandit Feedback
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Parametric Complexity Bounds for Approximating PDEs with Neural Networks
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Per-Pixel Classification is Not All You Need for Semantic Segmentation
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Revisiting ResNets: Improved Training and Scaling Strategies
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Sequence-to-Sequence Learning with Latent Neural Grammars
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Statistical Query Lower Bounds for List-Decodable Linear Regression
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How Well do Feature Visualizations Support Causal Understanding of CNN Activations?
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Multimodal and Multilingual Embeddings for Large-Scale Speech Mining
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Two steps to risk sensitivity
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Instance-Conditioned GAN
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GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
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Focal Attention for Long-Range Interactions in Vision Transformers
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List-Decodable Mean Estimation in Nearly-PCA Time
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Aligned Structured Sparsity Learning for Efficient Image Super-Resolution
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Neural Algorithmic Reasoners are Implicit Planners
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Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning
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Diffusion Models Beat GANs on Image Synthesis
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Bayesian decision-making under misspecified priors with applications to meta-learning
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Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization
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Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond
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Flexible Option Learning
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A Central Limit Theorem for Differentially Private Query Answering
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Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems
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Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms