Statistics Theory (math.ST)

How Well Generative Adversarial Networks Learn Distributions

Universal Prediction Band via Semi-Definite Programming

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

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

Just Interpolate: Kernel ''Ridgeless'' Regression Can Generalize

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

Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information