Statistics Theory (math.ST)

Interpolating Classifiers Make Few Mistakes

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

Universal Prediction Band via Semi-Definite Programming

High-Dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models

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

Deep Neural Networks for Estimation and Inference

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