Confidence regions in Wasserstein distributionally robust estimation J Blanchet, K Murthy, N Si Biometrika 109 (2), 295-315, 2022 | 63 | 2022 |
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 | 60 | 2020 |
Distributionally robust batch contextual bandits N Si, F Zhang, Z Zhou, J Blanchet Management Science 69 (10), 5772-5793, 2023 | 32 | 2023 |
Testing group fairness via optimal transport projections N Si, K Murthy, J Blanchet, VA Nguyen International Conference on Machine Learning, 9649-9659, 2021 | 25 | 2021 |
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 | 20 | 2023 |
Robust bayesian classification using an optimistic score ratio VA Nguyen, N Si, J Blanchet International Conference on Machine Learning, 7327-7337, 2020 | 16 | 2020 |
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 | 14 | 2020 |
On the foundation of distributionally robust reinforcement learning S Wang, N Si, J Blanchet, Z Zhou arXiv preprint arXiv:2311.09018, 2023 | 9 | 2023 |
Sample complexity of variance-reduced distributionally robust Q-learning S Wang, N Si, J Blanchet, Z Zhou arXiv preprint arXiv:2305.18420, 2023 | 9 | 2023 |
Optimal uncertainty size in distributionally robust inverse covariance estimation J Blanchet, N Si Operations Research Letters 47 (6), 618-621, 2019 | 7 | 2019 |
Optimal bidding and experimentation for multi-layer auctions in online advertising N Si, S Gultekin, J Blanchet, A Flores Available at SSRN 4358914, 2023 | 6 | 2023 |
Efficient steady-state simulation of high-dimensional stochastic networks J Blanchet, X Chen, N Si, PW Glynn Stochastic Systems 11 (2), 174-192, 2021 | 6 | 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 | 5 | 2016 |
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 | 3 | 2024 |
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 | 3 | 2023 |
Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems Y Fan, N Si, K Zhang arXiv preprint arXiv:2205.09809, 2022 | 3 | 2022 |
Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach N Si arXiv preprint arXiv:2310.17496, 2023 | 2 | 2023 |
Singular control of (reflected) Brownian motion: a computational method suitable for queueing applications B Ata, JM Harrison, N Si Queueing Systems, 1-37, 2024 | 1 | 2024 |
Selecting the Best Optimizing System N Si, Z Zheng arXiv preprint arXiv:2201.03065, 2022 | 1 | 2022 |
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 |