摘要：This talk will introduce and discuss generative flow networks (GFlowNets), a learning framework for amortized sampling, from a sequential decision-making perspective. Different from reinforcement learning, which optimizes trajectory-level statistics, GFlowNet targets sampling proportional to the reward function over the terminal states in a Markov decision process. A family of algorithms could thus be derived (as in RL). Fruitful connection with previous probabilistic methods and control can be drawn. We would also talk about its wide application in science in domains such as causal discovery, and drug discovery, and combinatorial optimization.
报告人简介：Dinghuai Zhang is a PhD candidate at Mila, advised by Prof. Aaron Courville and Prof. Yoshua Bengio. His research focuses on the intersection of probabilistic inference and scientific discovery. From a methodology perspective, he studies how to incorporate structured exploration into inference problems such as sampling, leveraging the power of the generative flow network (GFlowNet) framework which revolves around active learning, Bayesian inference, black box optimization, and reinforcement learning. He develops methods for applications on different sorts of scientific discovery tasks, including sequence design, molecule synthesis, and combinatorial optimization. Dinghuai also has spent time in FAIR lab (Meta AI). Dinghuai obtained a bachelor's degree in math from Peking University.
讨论班简介：北京大学应用数学青年讨论班 (Applied Mathematics Seminar for Youth) 是一个由北京大学卓越研究生计划组织的学术交流平台。该讨论班定期举办一系列读书会、学术报告，涵盖广泛的应用数学领域，旨在为应用数学领域的学生提供一个互相学习、交流和探讨的机会，促进学生们在该领域的学术成长和思维能力的培养。
报名问卷：我们提供午餐（从11:45开始），需要预定午餐的老师同学请填报名问卷 https://wj.qq.com/s2/13260352/ecac/
