Optimal subsampling for high-dimensional partially linear models via machine learning methods

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Decentralized Sparse Linear Regression via Gradient-Tracking

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Calibrated Inference: Statistical Inference that Accounts for Both Sampling Uncertainty and Distributional Uncertainty

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Relaxed Gaussian Process Interpolation: a Goal-Oriented Approach to Bayesian Optimization

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Linear Separation Capacity of Self-Supervised Representation Learning

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On the Convergence of Projected Policy Gradient for Any Constant Step Sizes

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Learning with Linear Function Approximations in Mean-Field Control

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A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization

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Model-free Change-Point Detection Using AUC of a Classifier

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EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback

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Multiple Instance Verification

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Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness

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Exponential Family Graphical Models: Correlated Replicates and Unmeasured Confounders, with Applications to fMRI Data

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Optimizing Return Distributions with Distributional Dynamic Programming

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Imprecise Multi-Armed Bandits: Representing Irreducible Uncertainty as a Zero-Sum Game

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Early Alignment in Two-Layer Networks Training is a Two-Edged Sword

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Hierarchical Decision Making Based on Structural Information Principles

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Generative Adversarial Networks: Dynamics

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"What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts

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Assumption-lean and data-adaptive post-prediction inference

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