| Quantifying distributional model risk via optimal transport J Blanchet, K Murthy Mathematics of Operations Research 44 (2), 565-600, 2019 | 437 | 2019 |
| Robust Wasserstein profile inference and applications to machine learning J Blanchet, Y Kang, K Murthy Journal of Applied Probability 56 (3), 830-857, 2019 | 373 | 2019 |
| A Markov chain approximation to choice modeling J Blanchet, G Gallego, V Goyal Operations Research 64 (4), 886-905, 2016 | 347 | 2016 |
| Efficient rare-event simulation for the maximum of heavy-tailed random walks J Blanchet, P Glynn | 142 | 2008 |
| On the Laplace transform of the lognormal distribution S Asmussen, JL Jensen, L Rojas-Nandayapa Methodology and Computing in Applied Probability 18 (2), 441-458, 2016 | 129 | 2016 |
| Asymptotic robustness of estimators in rare-event simulation P L'ecuyer, JH Blanchet, B Tuffin, PW Glynn ACM Transactions on Modeling and Computer Simulation (TOMACS) 20 (1), 1-41, 2010 | 114 | 2010 |
| Convergence rate analysis of a stochastic trust-region method via supermartingales J Blanchet, C Cartis, M Menickelly, K Scheinberg INFORMS journal on optimization 1 (2), 92-119, 2019 | 113 | 2019 |
| Distributionally robust mean-variance portfolio selection with Wasserstein distances J Blanchet, L Chen, XY Zhou Management Science 68 (9), 6382-6410, 2022 | 102 | 2022 |
| Learning in generalized linear contextual bandits with stochastic delays Z Zhou, R Xu, J Blanchet Advances in Neural Information Processing Systems 32, 2019 | 91 | 2019 |
| State-dependent importance sampling for rare-event simulation: An overview and recent advances J Blanchet, H Lam Surveys in Operations Research and Management Science 17 (1), 38-59, 2012 | 84 | 2012 |
| Towards optimal running times for optimal transport J Blanchet, A Jambulapati, C Kent, A Sidford arXiv preprint arXiv:1810.07717, 2018 | 71 | 2018 |
| State-dependent importance sampling for regularly varying random walks JH Blanchet, J Liu Advances in Applied Probability 40 (4), 1104-1128, 2008 | 71 | 2008 |
| Optimal transport-based distributionally robust optimization: Structural properties and iterative schemes J Blanchet, K Murthy, F Zhang Mathematics of Operations Research 47 (2), 1500-1529, 2022 | 69 | 2022 |
| Finite-sample regret bound for distributionally robust offline tabular reinforcement learning Z Zhou, Z Zhou, Q Bai, L Qiu, J Blanchet, P Glynn International Conference on Artificial Intelligence and Statistics, 3331-3339, 2021 | 69 | 2021 |
| Efficient Monte Carlo for high excursions of Gaussian random fields RJ Adler, JH Blanchet, J Liu | 66 | 2012 |
| Confidence regions in Wasserstein distributionally robust estimation J Blanchet, K Murthy, N Si Biometrika 109 (2), 295-315, 2022 | 64 | 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 | 61 | 2020 |
| Unbiased Monte Carlo for optimization and functions of expectations via multi-level randomization JH Blanchet, PW Glynn 2015 Winter Simulation Conference (WSC), 3656-3667, 2015 | 60 | 2015 |
| Efficient simulation of tail probabilities of sums of correlated lognormals S Asmussen, J Blanchet, S Juneja, L Rojas-Nandayapa Annals of Operations Research 189 (1), 5-23, 2011 | 60 | 2011 |
| Data-driven optimal transport cost selection for distributionally robust optimization J Blanchet, Y Kang, K Murthy, F Zhang 2019 winter simulation conference (WSC), 3740-3751, 2019 | 56 | 2019 |
Peter W. GlynnProfessor of Management Science and Engineering在 stanford.edu 的电子邮件经过验证
