Quantifying the Effects of Data Augmentation. - Kevin Han Huang; Peter Orbanz; Morgane Austern (CoRR, 2022)

A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing. - Kevin Han Huang; Xing Liu; Andrew B. Duncan; Axel Gandy (COLT, 2023)

Near-Optimality of Contrastive Divergence Algorithms. - Pierre Glaser; Kevin Han Huang; Arthur Gretton (NeurIPS, 2024)

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation. - Matthew Esmaili Mallory; Kevin Han Huang; Morgane Austern (COLT, 2025)

Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation. - Kevin Han Huang; Ni Zhan 001; Elif Ertekin; Peter Orbanz; Ryan P. Adams (CoRR, 2025)