2026年8月11日（周二）下午2:30，深圳河套学院荣幸地邀请到俄罗斯莫斯科高等经济大学计算机科学系的谢尔盖・萨姆索诺夫助理教授，在B301教室带来主题为“生成流网络的若干方法论”的学术报告分享。诚挚欢迎学院师生及对该领域感兴趣的产学研界同仁参与讲座。

Title

On Some Methodological Aspects of Generative Flow Networks

About the Speaker

Asst. Prof. Sergey Samsonov

Sergey Samsonov is currently the head of the International Laboratory of Stochastic Algorithms and High-Dimensional Inference and an assistant professor at the Department of Computer Science, HSE University, Moscow, Russia. He obtained his PhD degree in mathematics from HSE University in 2024. Sergey's research interests are mainly related to stochastic approximation, sampling methods, and reinforcement learning.

Sergey Samsonov has authored more than 30 papers in leading journals and conferences in the field, including COLT, ICML, NeurIPS, ICLR, AISTATS, the Journal of Machine Learning Research, and Mathematics of Operations Research.

Abstract

GFlowNets are a family of generative models that learn to sample objects from a given probability distribution, potentially known only up to a normalizing constant. The key concept behind GFlowNets is the use of two stochastic policies: a forward policy, which incrementally constructs compositional objects, and a backward policy, which sequentially deconstructs them. During inference, GFlowNets sample trajectories in an appropriately constructed directed acyclic graph environment.

This talk discusses several recent methodological applications of the GFlowNets. It introduces a theory that relaxes the acyclicity assumption, and shows how non-acyclic GFlowNets can be applied for solving the shortest path problem. This talk will also discuss how the methodology applies to sampling methods with continuous state space.

Host

Prof. Bingyi Jing

Date & Time

Tuesday, August 11, 2026,

2:30PM – 4:00PM

Venue

Room B401, Shenzhen Loop Area Institute

(6 Hongmian Rd, Fubao Sub-Street, Futian District, Shenzhen, navigate to "Shenzhen Loop Area Institute (South Gate)" on the map.)

Online link

Join the Meeting Online

Tencent Meeting：948-1135-4043