主讲嘉宾：香港科技大学王文佳博士
时 间：2024年4月19日14:00
地 点：信息楼510会议室
主 题：
Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High Dimensions
摘 要：
Nested simulation concerns estimating functionals of a conditional expectation via simulation. We propose a new method based on kernel ridge regression to exploit the smoothness of the conditional expectation as a function of the multidimensional conditioning variable. Asymptotic analysis shows that the proposed method can effectively alleviate the curse of dimensionality on the convergence rate as the simulation budget increases, provided that the conditional expectation is sufficiently smooth. The smoothness bridges the gap between the cubic root convergence rate (that is, the optimal rate for the standard nested simulation) and the square root convergence rate (that is, the canonical rate for the standard Monte Carlo simulation). We demonstrate the performance of the proposed method via numerical examples from portfolio risk management and input uncertainty quantification.
主讲人简介：
王文佳博士，是香港科技大学（广州）信息枢纽数据科学与分析学域的助理教授；2013年毕业于北京大学元培学院；2018年获得佐治亚理工学院工业工程系博士学位。2018-2020在美国统计与应用数据研究所/杜克大学任博士后。2020年加入香港科技大学（广州）。王文佳博士的研究方向包括计算机实验与不确定性量化、机器学习、非参数统计与统计学习在工业工程及生物科学中的应用。目前已在国际重要学术期刊发表20余篇，发表的期刊包括： Management Science, Journal of Machine Learning Research, SIAM/ASA Journal on Uncertainty Quantification, Journal of the American Statistical Association等。