Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning. - Chengchun Shi; Yunzhe Zhou; Lexin Li (CoRR, 2021)

The Infinitesimal Jackknife and Combinations of Models. - Indrayudh Ghosal; Yunzhe Zhou; Giles Hooker (CoRR, 2022)

A Generic Approach for Statistical Stability in Model Distillation. - Yunzhe Zhou; Peiru Xu; Giles Hooker (CoRR, 2022)

Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach. - Yunzhe Zhou; Zhengling Qi; Chengchun Shi; Lexin Li (AISTATS, 2023)

Testing for the Markov Property in Time Series via Deep Conditional Generative Learning. - Yunzhe Zhou; Chengchun Shi; Lexin Li; Qiwei Yao (CoRR, 2023)

A generic approach for reproducible model distillation. - Yunzhe Zhou; Peiru Xu; Giles Hooker (Mach. Learn., 2024)

Targeted Learning for Variable Importance. - Xiaohan Wang; Yunzhe Zhou; Giles Hooker (UAI, 2025)