@inproceedings{4bdab86b7d414c48a75b196076b0a4c8,
title = "Importance Sampling for Rare-Event Gradient Estimation",
abstract = "Importance sampling (IS) is a powerful tool for rare-event estimation. However, in many settings, we need to estimate not only the performance expectation but also its gradient. In this paper, we build a bridge from the IS for rare-event estimation to gradient estimation. We establish that, for a class of problems, an efficient IS sampler for estimating the probability of the underlying rare event is also efficient for estimating gradients of expectations over the same rare-event set. We show that both the infinitesimal perturbation analysis and the likelihood ratio estimators can be studied under the proposed framework. We use two numerical examples to validate our findings.",
author = "Yuanlu Bai and Shengyi He and Henry Lam and Guangxin Jiang and Fu, \{Michael C.\}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 Winter Simulation Conference, WSC 2022 ; Conference date: 11-12-2022 Through 14-12-2022",
year = "2022",
doi = "10.1109/WSC57314.2022.10015239",
language = "英语",
series = "Proceedings - Winter Simulation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3063--3074",
editor = "B. Feng and G. Pedrielli and Y. Peng and S. Shashaani and E. Song and C.G. Corlu and L.H. Lee and E.P. Chew and T. Roeder and P. Lendermann",
booktitle = "Proceedings of the 2022 Winter Simulation Conference, WSC 2022",
address = "美国",
}