时 间：2025年10月29日 10:00 - 11:00
地 点：普陀校区理科大楼A614
报告人：曹昊 复旦大学博士后
主持人：王天翔 华东师范大学助理教授
摘 要：
We present a kernel-based stochastic approximation (KBSA) framework for solving contextual stochastic optimization problems with differentiable objective functions. The framework only relies on system output estimates and can be applied to address a large class of contextual measures, including conditional expectations, conditional quantiles, CoVaR, and conditional expected shortfalls. Under appropriate conditions, we show the strong convergence of KBSA and characterize its finite-time performance in terms of bounds on the mean squared errors of the sequences of iterates produced. In addition, we discuss variants of the framework, including a version based on high-order kernels for further enhancing the convergence rate of the method and an extension of KBSA for handling contextual measures involving multiple conditioning events. Simulation experiments are also carried out to illustrate the framework.
报告人简介：
曹昊，复旦大学博士后，本科与博士毕业于复旦大学。研究方向为随机优化、强化学习、统计推断等。博士期间荣获首批国家自然科学基金青年学生基础研究项目（博士研究生），研究工作发表于Automatica，INFORMS Journal on Computing等顶级期刊。