【学术报告】Exploring Stochastic Methods For Deep Learning and Reinforcement Learning

• 主讲人： Zaiwen Wen, Beijing International Center for Mathematical Research.
• 时间：周四21:00-22:00，2021-01-14
• 地点：online

Beijing-Saint Petersburg Mathematics Colloquium (online)

摘要（Abstract） 

Stochastic methods are widely used in machine learning. In this talk, we present a structured stochastic quasi-Newton method and a sketchy empirical natural gradient method for deep learning. We also introduce a stochastic quadratic penalty algorithm for reinforcement learning.

主讲人简介（Bio）

 Wen's research interests include large-scale computational optimization and their applications in data sciences. Together with his coauthors, he has developed both deterministic and stochastic semi-smooth Newton algorithms for composite convex program and Newton type algorithms for Riemannian optimization, as well as academic software packages such as SSNSDP, ARNT, Arrabit, LMSVD and LMAFIT, etc. He was awarded the Science and Technology Award for Chinese Youth in 2016, and the Beijing Science and Technology Prize-Outstanding Youth Scholar Zhongguan Village Prize in 2020. He is an associate editor of Journal of the Operations Research Society of China, Journal of Computational Mathematics and a technical editor of Mathematical Programming Computation.