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Preprints and Publications
A/B Tests and Experimental Design
Bias Analysis of Experiments for Multi-Item Multi-Period Inventory Control Policies
Xinqi Chen, Xingyu Bai, Zeyu Zheng, Nian Si
Experimental Design in Live-Interaction Platforms
Chenran Weng, Xiao Lei, Nian Si
Seller-Side Experiments under Interference Induced by Feedback Loops in Two-Sided Platforms
Zhihua Zhu, Zheng Cai, Liang Zheng, Nian Si
Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted
Training Approach
Nian Si
A/B Tests Under a Safety Budget: A Simulation-Optimization Point of View
Nian Si, Jose Blanchet, Ramesh Johari, Zeyu Zheng
preprint
Optimal Bidding and Experimentation for Multi-layer Auctions in Online Advertising
Nian Si, San Gultekin, Jose Blanchet, Aaron Flores
Distributional Robustness and Optimal Transport
Knowledge-Guided Wasserstein Distributionally Robust Optimization
Zitao Wang, Ziyuan Wang, Molei Liu, Nian Si
Tractable Robust Markov Decision Processes
Julien Grand-Clément, Nian Si, Shengbo Wang
Multi-source Stable Variable Importance Measure via Adversarial Machine Learning
Zitao Wang, Nian Si, Zijian Guo, Molei Liu
Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces
Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou
Artificial Intelligence and Statistics Conference (AISTATS) , 2025
Oral presentation; top 2% of submissions
On the Foundation of Distributionally Robust Reinforcement Learning
Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou
Sample Complexity of Variance-reduced Distributionally Robust Q-learning
Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou
Journal of Machine Learning Research, 2024
A Finite Sample Complexity Bound for Distributionally Robust Q-learning
Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou
Artificial Intelligence and Statistics Conference (AISTATS) , 2023
Distributional Robust Batch Contextual Bandits
Nian Si, Fan Zhang, Zhengyuan Zhou, Jose Blanchet
Management Science, 2023
2021 MSOM Student Paper Prize Finalist
Confidence Regions in Wasserstein Distributionally Robust Estimation
Jose Blanchet, Karthyek Murthy, Nian Si*
Biometrika, 2021
Optimal Uncertainty Size in Distributionally Robust Inverse Covariance Estimation
Jose Blanchet, Nian Si*
Operations Research Letters, 2019
Robust Strategic Transfer Learning in an Uncertain Environment: from Second- to
First-price Auctions
Nian Si, San Gultekin, Jose Blanchet, Aaron Flores
Testing Group Fairness via Optimal Transport Projections
Nian Si, Karthyek Murthy, Jose Blanchet, Viet Anh Nguyen
International Conference on Machine Learning (ICML), 2021
Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality
Nian Si, Jose Blanchet, Soumyadip Ghosh, Mark Squillante
Neural Information Processing Systems (NeurIPS), 2020
Spotlight presentation; top 4% of submissions
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits
Nian Si†, Fan Zhang†, Zhengyuan Zhou, Jose Blanchet
International Conference on Machine Learning (ICML), 2020
Robust Bayesian Classification Using an Optimistic Score Ratio
Viet Anh Nguyen, Nian Si, Jose Blanchet
International Conference on Machine Learning (ICML), 2020
Numerical Solutions to Large Scale Operations Problems
Simulation
Machine Learning and Platform Operations
Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
Yewen Fan†, Nian Si†, Kun Zhang
International Conference on Learning Representations (ICLR) , 2023
Calibration-then-Calculation: A Variance Reduced Metric Framework in Deep Click-Through Rate Prediction Models
Yewen Fan, Nian Si, Xiangchen Song, Kun Zhang
ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycenters
Hao Liu, Junze (Tony) Ye, Jose Blanchet, Nian Si
Artificial Intelligence and Statistics Conference (AISTATS) , 2025
Oral presentation; top 2% of submissions
* stands for alphabetical order.
† stands for equal contribution.
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