Rui Gao

According to our database, Rui Gao authored at least 30 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Global Convergence of Wasserstein Policy Gradient for Entropy-Regularized Reinforcement Learning.

Zhaoyu Zhu, Rui Gao, Shuang Li

CoRR, May, 2026

Wasserstein Proximal Policy Gradient.

Zhaoyu Zhu, Shuhan Zhang, Rui Gao, Shuang Li

CoRR, March, 2026

Sinkhorn Distributionally Robust Optimization.

Jie Wang, Rui Gao, Yao Xie

Oper. Res., 2026

2025

A Short and General Duality Proof for Wasserstein Distributionally Robust Optimization.

Luhao Zhang, Jincheng Yang, Rui Gao

Oper. Res., 2025

Reproducing Kernel Hilbert Space Choice Model.

Yiqi Yang, Zhi Wang, Rui Gao, Shuang Li

Proceedings of the 26th ACM Conference on Economics and Computation, 2025

DeepHalo: A Neural Choice Model with Controllable Context Effects.

Shuhan Zhang, Zhi Wang, Rui Gao, Shuang Li

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Convergence of Mean-Field Langevin Stochastic Descent-Ascent for Distributional Minimax Optimization.

Zhangyi Liu, Feng Liu, Rui Gao, Shuang Li

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Optimal Robust Policy for Feature-Based Newsvendor.

Luhao Zhang, Jincheng Yang, Rui Gao

Manag. Sci., 2024

Wasserstein Distributionally Robust Optimization and Variation Regularization.

Rui Gao, Xi Chen, Anton J. Kleywegt

Oper. Res., 2024

Reliable Off-Policy Evaluation for Reinforcement Learning.

Jie Wang, Rui Gao, Hongyuan Zha

Oper. Res., 2024

Data-driven Multistage Distributionally Robust Linear Optimization with Nested Distance.

Rui Gao, Rohit Arora, Yizhe Huang

CoRR, 2024

Non-Convex Robust Hypothesis Testing Using Sinkhorn Uncertainty Sets.

Jie Wang, Rui Gao, Yao Xie

Proceedings of the IEEE International Symposium on Information Theory, 2024

2023

Distributionally Robust Stochastic Optimization with Wasserstein Distance.

Rui Gao, Anton J. Kleywegt

Math. Oper. Res., May, 2023

Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels.

Hao Wang, Rui Gao, Flávio P. Calmon

J. Mach. Learn. Res., 2023

Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality.

Rui Gao

Oper. Res., 2023

Aleatoric and Epistemic Discrimination in Classification.

Hao Wang, Luxi He, Rui Gao, Flávio P. Calmon

CoRR, 2023

Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions.

Hao Wang, Luxi He, Rui Gao, Flávio P. Calmon

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

A Simple Duality Proof for Wasserstein Distributionally Robust Optimization.

Luhao Zhang, Jincheng Yang, Rui Gao

CoRR, 2022

Distributionally robust weighted k-nearest neighbors.

Shixiang Zhu, Liyan Xie, Minghe Zhang, Rui Gao, Yao Xie

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Two-Sample Test with Kernel Projected Wasserstein Distance.

Jie Wang, Rui Gao, Yao Xie

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Learning While Dissipating Information: Understanding the Generalization Capability of SGLD.

Hao Wang, Yizhe Huang, Rui Gao, Flávio P. Calmon

CoRR, 2021

Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators.

Qitian Wu, Rui Gao, Hongyuan Zha

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels.

Hao Wang, Yizhe Huang, Rui Gao, Flávio P. Calmon

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Generalization Bounds for (Wasserstein) Robust Optimization.

Yang An, Rui Gao

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Two-sample Test using Projected Wasserstein Distance.

Jie Wang, Rui Gao, Yao Xie

Proceedings of the IEEE International Symposium on Information Theory, 2021

2020

Two-sample Test using Projected Wasserstein Distance: Breaking the Curse of Dimensionality.

Jie Wang, Rui Gao, Yao Xie

CoRR, 2020

Distributionally Robust $k$-Nearest Neighbors for Few-Shot Learning.

Shixiang Zhu, Liyan Xie, Minghe Zhang, Rui Gao, Yao Xie

CoRR, 2020

2019

Stein Bridging: Enabling Mutual Reinforcement between Explicit and Implicit Generative Models.

Qitian Wu, Rui Gao, Hongyuan Zha

CoRR, 2019

2018

Robust Hypothesis Testing Using Wasserstein Uncertainty Sets.

Rui Gao, Liyan Xie, Yao Xie, Huan Xu

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Wasserstein Distributional Robustness and Regularization in Statistical Learning.

Rui Gao, Xi Chen, Anton J. Kleywegt

CoRR, 2017