Machine Learning (stat.ML)

Online Learning to Transport via the Minimal Selection Principle

Universal Prediction Band via Semi-Definite Programming

A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-L1-Norm Interpolated Classifiers

Mehler’s Formula, Branching Process, and Compositional Kernels of Deep Neural Networks

Reversible Gromov-Monge Sampler for Simulation-Based Inference

How Well Generative Adversarial Networks Learn Distributions

Interpolating Classifiers Make Few Mistakes

Deep Neural Networks for Estimation and Inference

Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits

On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels