Nian Si
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Confidence regions in wasserstein distributionally robust estimation
J Blanchet, K Murthy, N Si
arXiv preprint arXiv:1906.01614, 2019
Distributionally robust policy evaluation and learning in offline contextual bandits
N Si, F Zhang, Z Zhou, J Blanchet
International Conference on Machine Learning, 8884-8894, 2020
Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality.
N Si, JH Blanchet, S Ghosh, MS Squillante
NeurIPS, 2020
Optimal uncertainty size in distributionally robust inverse covariance estimation
J Blanchet, N Si
Operations Research Letters 47 (6), 618-621, 2019
Efficient Steady-State Simulation of High-Dimensional Stochastic Networks
J Blanchet, X Chen, N Si, PW Glynn
Stochastic Systems, 2021
Efficient computation of the likelihood expansions for diffusion models
C Li, Y An, D Chen, Q Lin, N Si
IIE Transactions 48 (12), 1156-1171, 2016
Robust bayesian classification using an optimistic score ratio
VA Nguyen, N Si, J Blanchet
International Conference on Machine Learning, 7327-7337, 2020
Distributional Robust Batch Contextual Bandits
N Si, F Zhang, Z Zhou, J Blanchet
arXiv preprint arXiv:2006.05630, 2020
Testing Group Fairness via Optimal Transport Projections
N Si, K Murthy, J Blanchet, VA Nguyen
arXiv preprint arXiv:2106.01070, 2021
DRA: Distributionally Robust Adversarial Training
N Si, MSE Stanford, F Zhang, T Zhang
Appendix–Robust Bayesian Classification Using an Optimistic Score Ratio
VA Nguyen, N Si, J Blanchet
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