Sledovat
Nian Si
Nian Si
University of Chicago Booth School of Business
E-mailová adresa ověřena na: chicagobooth.edu - Domovská stránka
Název
Citace
Citace
Rok
Confidence regions in Wasserstein distributionally robust estimation
J Blanchet, K Murthy, N Si
Biometrika 109 (2), 295-315, 2022
642022
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
612020
Distributionally robust batch contextual bandits
N Si, F Zhang, Z Zhou, J Blanchet
Management Science 69 (10), 5772-5793, 2023
322023
Testing group fairness via optimal transport projections
N Si, K Murthy, J Blanchet, VA Nguyen
International Conference on Machine Learning, 9649-9659, 2021
242021
A finite sample complexity bound for distributionally robust q-learning
S Wang, N Si, J Blanchet, Z Zhou
International Conference on Artificial Intelligence and Statistics, 3370-3398, 2023
212023
Robust bayesian classification using an optimistic score ratio
VA Nguyen, N Si, J Blanchet
International Conference on Machine Learning, 7327-7337, 2020
152020
Quantifying the empirical wasserstein distance to a set of measures: Beating the curse of dimensionality
N Si, J Blanchet, S Ghosh, M Squillante
Advances in Neural Information Processing Systems 33, 21260-21270, 2020
152020
On the foundation of distributionally robust reinforcement learning
S Wang, N Si, J Blanchet, Z Zhou
arXiv preprint arXiv:2311.09018, 2023
92023
Sample complexity of variance-reduced distributionally robust Q-learning
S Wang, N Si, J Blanchet, Z Zhou
arXiv preprint arXiv:2305.18420, 2023
92023
Optimal uncertainty size in distributionally robust inverse covariance estimation
J Blanchet, N Si
Operations Research Letters 47 (6), 618-621, 2019
72019
Efficient steady-state simulation of high-dimensional stochastic networks
J Blanchet, X Chen, N Si, PW Glynn
Stochastic Systems 11 (2), 174-192, 2021
62021
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
62016
Optimal bidding and experimentation for multi-layer auctions in online advertising
N Si, S Gultekin, J Blanchet, A Flores
Available at SSRN 4358914, 2023
42023
Drift control of high-dimensional rbm: A computational method based on neural networks
B Ata, JM Harrison, N Si
arXiv preprint arXiv:2309.11651, 2023
32023
Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
Y Fan, N Si, K Zhang
arXiv preprint arXiv:2205.09809, 2022
22022
Singular control of (reflected) Brownian motion: a computational method suitable for queueing applications
B Ata, JM Harrison, N Si
Queueing Systems, 1-37, 2024
12024
Seller-Side Experiments under Interference Induced by Feedback Loops in Two-Sided Platforms
Z Zhu, Z Cai, L Zheng, N Si
arXiv preprint arXiv:2401.15811, 2024
12024
Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach
N Si
arXiv preprint arXiv:2310.17496, 2023
12023
Selecting the Best Optimizing System
N Si, Z Zheng
arXiv preprint arXiv:2201.03065, 2022
12022
Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces
S Wang, N Si, J Blanchet, Z Zhou
arXiv preprint arXiv:2406.11281, 2024
2024
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Články 1–20