时 间：2023年9月21日 15:00-16:00
地 点：普陀校区理科大楼A1514
报告人：朱裕华 加州大学圣地亚哥分校 助理教授
主持人：王光辉 华东师范大学 副教授
摘 要：
Yuhua Zhu is an assistant professor at UC San Diego, where she holds a joint appointment in the Halicioğlu Data Science Institute (HDSI) and the Department of Mathematics. Previously, she was a Postdoctoral Fellow at Stanford University mentored by Lexing Ying. She earned her Ph.D. from UW-Madison in 2019 advised by Shi Jin, and she obtained her BS in Mathematics from SJTU in 2014. Her work builds the bridge between differential equations and machine learning, spanning the areas of reinforcement learning, stochastic optimization, sequential decision-making, and uncertainty quantification.
报告人简介：
In this talk, I will explore the rich interplay between differential equations and machine learning. I will highlight the use of collective dynamics and partial differential equations as powerful tools for improving machine learning algorithms and models. (i) In the first half of the talk, I will introduce a novel dynamical system that draws inspiration from collective intelligence observed in biology. This system offers a compelling alternative to gradient-based optimization. It enables gradient-free optimization to efficiently find global minimum in non-convex optimization problems. (ii) In the second half of the talk, I will build the connection between Hamilton-Jacobi-Bellman equations and the multi-armed bandit (MAB) problems. MAB is a widely used paradigm for studying the exploration-exploitation trade-off in sequential decision making under uncertainty. This is the first work that establishes this connection in a general setting. I will present an efficient algorithm for solving MAB problems based on this connection and demonstrate its practical applications.