SPEAKER：黄志源，助理教授，同济大学经济与管理学院

TIME/DATE: 2024年11月15日10:00-11:00

TENCENT：322 833 308

PW：592096

ABSTRACT

In rare-event simulation, an importance sampling (IS) estimator is regarded as efficient if its relative error, namely the ratio between its standard deviation and mean, is sufficiently controlled. It is widely known that when a rare-event set contains multiple “important regions” encoded by the dominating points, IS needs to account for all of them via mixing to achieve efficiency. We argue that missing less significant dominating points may not necessarily cause inefficiency, and the traditional analysis recipe could suffer from intrinsic looseness by using relative error, or in turn estimation variance, as an efficiency criterion. We propose a new efficiency notion called “probabilistic efficiency” to tighten this gap. The new notion is especially relevant in high-dimensional settings where the computational effort to locate all dominating points is enormous.

GUEST BIO

黄志源现任同济大学经济与管理学院助理教授，他的主要研究为数据驱动的鲁棒优化、稀有事件仿真以及相关方法在人工智能系统与机器学习问题中的应用。