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

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  2. Linear Separation Capacity of Self-Supervised Representation Learning

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

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  4. Learning with Linear Function Approximations in Mean-Field Control

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

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

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

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  8. Multiple Instance Verification

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

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

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  11. Optimizing Return Distributions with Distributional Dynamic Programming

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

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

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  14. Hierarchical Decision Making Based on Structural Information Principles

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  15. Generative Adversarial Networks: Dynamics

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

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  17. Assumption-lean and data-adaptive post-prediction inference

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  18. Bagged Regularized k-Distances for Anomaly Detection

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  19. Four Axiomatic Characterizations of the Integrated Gradients Attribution Method

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  20. Fast Algorithm for Constrained Linear Inverse Problems

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