Sparse SVM with Hard-Margin Loss: a Newton-Augmented Lagrangian Method in Reduced Dimensions

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On Model Identification and Out-of-Sample Prediction of PCR with Applications to Synthetic Controls

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Bayesian Scalar-on-Image Regression with a Spatially Varying Single-layer Neural Network Prior

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DRM Revisited: A Complete Error Analysis

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Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF

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Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers

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Score-based Causal Representation Learning: Linear and General Transformations

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On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension

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Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms

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Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles

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Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos

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On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control

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A Decentralized Proximal Gradient Tracking Algorithm for Composite Optimization on Riemannian Manifolds

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Learning conditional distributions on continuous spaces

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A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs

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Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities

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Linear Hypothesis Testing in High-Dimensional Expected Shortfall Regression with Heavy-Tailed Errors

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Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling

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Distribution Free Tests for Model Selection Based on Maximum Mean Discrepancy with Estimated Parameters

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Statistical field theory for Markov decision processes under uncertainty

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