Publications

Note: † indicates a student co-author

Journal Articles

• Decision-making with Side Information: A Causal Transport Robust Approach, Operations Research, 2026.
  Jincheng Yang†, Luhao Zhang†, Ningyuan Chen, Rui Gao, Ming Hu

• Extreme yet Plausible: Choosing Scenarios to Stress Test Financial Institutions, Management Science, 2025.
  Rohit Arora†, Rui Gao, Stathis Tompaidis
  

  • Honorable Mention (2nd place) in INFORMS Section on Finance Best Student Paper Competition 2020 (awarded to Rohit)
    

• Sinkhorn Distributionally Robust Optimization, Operations Research, 2025.
  Jie Wang†, Rui Gao, Yao Xie
  

  • Winner in INFORMS Best Poster Award 2022 (awarded to Jie, 2 out of ~200)
    

• A Short and General Duality Proof for Wasserstein Distributionally Robust Optimization, Operations Research, 2024.
  Luhao Zhang†, Jincheng Yang†, Rui Gao

• Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels, Journal of Machine Learning Research, 2023.
  Hao Wang†, Rui Gao, Flavio du Pin Calmon
  

• Optimal Robust Policy for Feature-Based Newsvendor, Management Science, 2023.
  Luhao Zhang†, Jincheng Yang†, Rui Gao

  • Young Researcher Prize in INFORMS Optimization Society 2026
    

  • Selected to present at MSOM Conference 2021 SIG Meetings on Supply Chain Management
    

• Reliable Off-policy Evaluation for Reinforcement Learning, Operations Research, 2022.
  Jie Wang†, Rui Gao, Hongyuan Zha

• Wasserstein Distributionally Robust Optimization and Variation Regularization, Operations Research, 2022.
  Rui Gao, Xi Chen, Anton J. Kleywegt
  

  • Winner in INFORMS Data Mining Best Paper Award 2017
    

  • Runner-up in INFORMS Computing Society Student Paper Award 2017
    

• Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality, Operations Research, 2022.
  Rui Gao
  

  • Winner in INFORMS Junior Faculty Interest Group (JFIG) Paper Competition 2020
    

• Distributionally Robust Stochastic Optimization with Wasserstein Distance, Mathematics of Operations Research, 2022.
  Rui Gao, Anton J. Kleywegt
  

  • Finalist in INFORMS George Nicholson Student Paper Competition 2016
    

• Risk-based Distributionally Robust Optimal Gas-power Flow with Wasserstein Distance, IEEE Transactions on Power Systems, 2019.
  Cheng Wang†, Rui Gao, Wei Wei, Miadreza Shafie-khah, Tianshu Bi, Joao PS Catalao

• Risk-based Distributionally Robust Optimal Power Flow with Dynamic Line Rating, IEEE Transactions on Power Systems, 2018.
  Cheng Wang†, Rui Gao, Feng Qiu, Jianhui Wang, Linwei Xin
  

  • Best Paper Award for papers published from 2017 through 2019 (7 out of ~1800)
    

Conference Proceedings

• Neural Assortment Optimization, EC, 2026.
  Zhen Yang, Rick Liang, Zhi Wang, Yang An, Rui Gao, Shuang Li
  

• DeepHalo: A Neural Choice Model with Controllable Context Effects, NeurIPS, 2025.
  Shuhan Zhang†, Zhi Wang†, Rui Gao, Shuang Li
  

  • Spotlight (acceptance rate 3.19%)
    

• Convergence of Mean-Field Langevin Stochastic Gradient Descent-Ascent for Distributional Minimax Optimization, ICML, 2025.
  Zhangyi Liu†, Feng Liu†, Rui Gao, Shuang Li
  

  • Spotlight (acceptance rate 2.59%)
    

• Reproducing Kernel Hilbert Space Choice Model, EC, 2025.
  Yiqi Yang†, Zhi Wang†, Rui Gao, Shuang Li
  

• Aleatoric and Epistemic Discrimination in Classification, NeurIPS, 2023.
  Hao Wang†, Luxi He†, Rui Gao, Flavio du Pin Calmon
  

  • Spotlight (acceptance rate 3.06%)
    

• Distributionally Robust Weighted k-Nearest Neighbors, NeurIPS, 2022.
  Shixiang Zhu†, Liyan Xie†, Minghe Zhang†, Rui Gao, Yao Xie

• Two-sample Test with Kernel Projected Wasserstein Distance, AISTATS, 2022.
  Jie Wang†, Rui Gao, Yao Xie
  

  • Oral presentation (acceptance rate 2.6%)
    

• Generalization Bounds for (Wasserstein) Robust Optimization, NeurIPS, 2021.
  Yang An†, Rui Gao
  

• Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels, NeurIPS, 2021.
  Hao Wang†, Yizhe Huang†, Rui Gao, Flavio du Pin Calmon
  

• Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators, NeurIPS, 2021.
  Qitian Wu†, Rui Gao, Hongyuan Zha
  

• Robust Hypothesis Testing Using Wasserstein Uncertainty Sets, NeurIPS, 2018.
  Rui Gao, Liyan Xie, Yao Xie, Huan Xu
  

  • Spotlight presentation (acceptance rate 3.5%)
    

  • Runner-up in INFORMS Computing Society Student Paper Prize 2019 (awarded to Liyan)
    

Preprints

• Neural-Network Mixed Logit Choice Model: Statistical and Optimality Guarantees, 2024.
  Zhi Wang†, Rui Gao, Shuang Li
  

  • Second place in POMS-HK 2025 Best Student Paper Competition (awarded to Zhi)
    

• Statistical Inference of Contextual Stochastic Optimization with Endogenous Uncertainty, 2024.
  Junyu Cao, Rui Gao and Zhen Yang†

• Entropic Regularization for Adversarial Robust Learning, 2023.
  Jie Wang†, Rui Gao, Yao Xie
  

  • Winner in INFORMS Data Mining and Decision Analytics Workshop Best Paper Competition Award (awarded to Jie, 2 out of 57)
    

• Wasserstein Regularization for 0-1 loss, 2022.
  Zhen Yang†, Rui Gao

• Data-driven Multistage Linear Distributionally Robust Optimization with Nested Distance, 2022.
  Rui Gao, Rohit Arora†, Yizhe Huang†

• Robust Hypothesis Testing with Wasserstein Uncertainty Sets, 2021.
  Liyan Xie†, Rui Gao, Yao Xie