Si, Nian

11 publications

ICML 2025 Knowledge-Guided Wasserstein Distributionally Robust Optimization Zitao Wang, Ziyuan Wang, Molei Liu, Nian Si

NeurIPS 2025 Sample Complexity of Distributionally Robust Average-Reward Reinforcement Learning Zijun Chen, Shengbo Wang, Nian Si

AISTATS 2025 ScoreFusion: Fusing Score-Based Generative Models via Kullback–Leibler Barycenters Hao Liu, Tony Junze Ye, Jose Blanchet, Nian Si

AISTATS 2025 Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou

JMLR 2024 Sample Complexity of Variance-Reduced Distributionally Robust Q-Learning Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou

AISTATS 2023 A Finite Sample Complexity Bound for Distributionally Robust Q-Learning Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou

ICLR 2023 Calibration Matters: Tackling Maximization Bias in Large-Scale Advertising Recommendation Systems Yewen Fan, Nian Si, Kun Zhang

ICML 2021 Testing Group Fairness via Optimal Transport Projections Nian Si, Karthyek Murthy, Jose Blanchet, Viet Anh Nguyen

ICML 2020 Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits Nian Si, Fan Zhang, Zhengyuan Zhou, Jose Blanchet

NeurIPS 2020 Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality Nian Si, Jose Blanchet, Soumyadip Ghosh, Mark Squillante

ICML 2020 Robust Bayesian Classification Using an Optimistic Score Ratio Viet Anh Nguyen, Nian Si, Jose Blanchet