Learning (cs.LG)

Reversible Gromov-Monge Sampler for Simulation-Based Inference

Interpolating Classifiers Make Few Mistakes

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria

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

How Well Generative Adversarial Networks Learn Distributions

Deep Neural Networks for Estimation and Inference

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