| 2025 | AISTATS | ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycenters. | Hao Liu, Junze Ye, Jose H. Blanchet, Nian Si |
| 2025 | AISTATS | Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces. | Shengbo Wang, Nian Si, Jose H. Blanchet, Zhengyuan Zhou |
| 2025 | ICML | Knowledge-Guided Wasserstein Distributionally Robust Optimization. | Zitao Wang, Ziyuan Wang, Molei Liu, Nian Si |
| 2023 | AISTATS | A Finite Sample Complexity Bound for Distributionally Robust Q-learning. | Shengbo Wang, Nian Si, Jos H. Blanchet, Zhengyuan Zhou |
| 2023 | ICLR | Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems. | Yewen Fan, Nian Si, Kun Zhang |
| 2023 | WSC | A Preliminary Study of Regularization Framework for Constructing Task-Specific Simulators. | Dilara Aykanat, Zeyu Zheng, Nian Si |
| 2021 | ICML | Testing Group Fairness via Optimal Transport Projections. | Nian Si, Karthyek Murthy, Jose H. Blanchet, Viet Anh Nguyen |
| 2020 | ICML | Robust Bayesian Classification Using An Optimistic Score Ratio. | Viet Anh Nguyen, Nian Si, Jose H. Blanchet |
| 2020 | ICML | Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits. | Nian Si, Fan Zhang, Zhengyuan Zhou, Jose H. Blanchet |