The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise

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Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick

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Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables

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Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions

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Regularizing Hard Examples Improves Adversarial Robustness

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Random ReLU Neural Networks as Non-Gaussian Processes

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Riemannian Bilevel Optimization

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Supervised Learning with Evolving Tasks and Performance Guarantees

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Error estimation and adaptive tuning for unregularized robust M-estimator

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From Sparse to Dense Functional Data in High Dimensions: Revisiting Phase Transitions from a Non-Asymptotic Perspective

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Locally Private Causal Inference for Randomized Experiments

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Estimating Network-Mediated Causal Effects via Principal Components Network Regression

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Selective Inference with Distributed Data

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Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization

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An Axiomatic Definition of Hierarchical Clustering

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Test-Time Training on Video Streams

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Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

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A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation

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Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents

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Enhancing Graph Representation Learning with Localized Topological Features

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