Optimization and Control (math.OC)

Randomization Inference When N Equals One

High-Dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models

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

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

Statistical Inference for the Population Landscape via Moment Adjusted Stochastic Gradients

Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks

Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability

Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions