Wasserstein Distributional Robustness and Regularization in Statistical Learning. - Rui Gao 001; Xi Chen 010; Anton J. Kleywegt (CoRR, 2017)

Robust Hypothesis Testing Using Wasserstein Uncertainty Sets. - Rui Gao 001; Liyan Xie; Yao Xie 002; Huan Xu (NeurIPS, 2018)

Stein Bridging: Enabling Mutual Reinforcement between Explicit and Implicit Generative Models. - Qitian Wu; Rui Gao 001; Hongyuan Zha (CoRR, 2019)

Distributionally Robust $k$-Nearest Neighbors for Few-Shot Learning. - Shixiang Zhu; Liyan Xie; Minghe Zhang; Rui Gao 001; Yao Xie 002 (CoRR, 2020)

Two-sample Test using Projected Wasserstein Distance: Breaking the Curse of Dimensionality. - Jie Wang 049; Rui Gao 001; Yao Xie 002 (CoRR, 2020)

Two-sample Test using Projected Wasserstein Distance. - Jie Wang 049; Rui Gao 001; Yao Xie 002 (ISIT, 2021)

Generalization Bounds for (Wasserstein) Robust Optimization. - Yang An; Rui Gao 001 (NeurIPS, 2021)

Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels. - Hao Wang 063; Yizhe Huang; Rui Gao 001; Flávio P. Calmon (NeurIPS, 2021)

Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators. - Qitian Wu; Rui Gao 001; Hongyuan Zha (NeurIPS, 2021)

Learning While Dissipating Information: Understanding the Generalization Capability of SGLD. - Hao Wang 063; Yizhe Huang; Rui Gao 001; Flávio P. Calmon (CoRR, 2021)

Sinkhorn Distributionally Robust Optimization. - Jie Wang 049; Rui Gao 001; Yao Xie 002 (CoRR, 2021)

Two-Sample Test with Kernel Projected Wasserstein Distance. - Jie Wang 049; Rui Gao 001; Yao Xie 002 (AISTATS, 2022)

Distributionally robust weighted k-nearest neighbors. - Shixiang Zhu; Liyan Xie; Minghe Zhang; Rui Gao 001; Yao Xie 002 (NeurIPS, 2022)

A Simple Duality Proof for Wasserstein Distributionally Robust Optimization. - Luhao Zhang; Jincheng Yang; Rui Gao 001 (CoRR, 2022)

Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions. - Hao Wang 063; Luxi He; Rui Gao 001; Flávio P. Calmon (NeurIPS, 2023)

Aleatoric and Epistemic Discrimination in Classification. - Hao Wang 063; Luxi He; Rui Gao 001; Flávio P. Calmon (CoRR, 2023)

Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality. - Rui Gao 001 (Oper. Res., 2023)

Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels. - Hao Wang 063; Rui Gao 001; Flávio P. Calmon (J. Mach. Learn. Res., 2023)

Distributionally Robust Stochastic Optimization with Wasserstein Distance. - Rui Gao 001; Anton J. Kleywegt (Math. Oper. Res., 2023)

Non-Convex Robust Hypothesis Testing Using Sinkhorn Uncertainty Sets. - Jie Wang 049; Rui Gao 001; Yao Xie 002 (ISIT, 2024)

Data-driven Multistage Distributionally Robust Linear Optimization with Nested Distance. - Rui Gao 001; Rohit Arora; Yizhe Huang (CoRR, 2024)

Reliable Off-Policy Evaluation for Reinforcement Learning. - Jie Wang 049; Rui Gao 001; Hongyuan Zha (Oper. Res., 2024)

Wasserstein Distributionally Robust Optimization and Variation Regularization. - Rui Gao 001; Xi Chen 010; Anton J. Kleywegt (Oper. Res., 2024)

Optimal Robust Policy for Feature-Based Newsvendor. - Luhao Zhang; Jincheng Yang; Rui Gao 001 (Manag. Sci., 2024)

Reproducing Kernel Hilbert Space Choice Model. - Yiqi Yang; Zhi Wang; Rui Gao 001; Shuang Li 002 (EC, 2025)

A Short and General Duality Proof for Wasserstein Distributionally Robust Optimization. - Luhao Zhang; Jincheng Yang; Rui Gao 001 (Oper. Res., 2025)