GFlowNets: Exploration for Structured Probabilistic Inference
- Dinghuai Zhang, Mila
- Time: 2023-10-11 16:00
- Host: Turing Class Research Committee
- Venue: Room 204, Courtyard No.5, Jingyuan
Abstract
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.
Biography
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.