Yunwen Lei

"Statistical Learning by Stochastic Gradient Descent"

( MPI-SWS talk in Kooperation mit dem Fachbereich Informatik)

Stochastic gradient descent (SGD) has become the workhorse behind many machine learning problems. Optimization and sampling errors are two contradictory factors responsible for the statistical behavior of SGD. In this talk, we report our generalization analysis of SGD by considering simultaneously the optimization and sampling errors. We remove some restrictive assumptions in the literature and significantly improve the existing generalization bounds. Our results help to understand how to stop SGD early to get a best statistical behavior.

Bio: Dr. Yunwen Lei got his PhD degree from Wuhan University in 2014. He is now a Lecturer at School of Computer Science, University of Birmingham. Previously, he was a Humboldt Research Fellow at University of Kaiserslautern, a Research Assistant Professor at Southern University of Science and Technology, and a Postdoctoral Research Fellow at City University of Hong Kong. Dr. Lei's research interests include learning theory and optimization. He has published papers in ICML, NeurIPS, ICLR and some prestigious journals including IEEE Transactions on Information Theory and Journal of Machine Learning Research.

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