SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Diffe

组织者 Organizer:Chris Junchi Li
时间 Time: 周五13:30-15:05,2019-9-6
地点 Venue:清华大学近春园西楼第一会议室

讨论班简介 Description

Title: SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator
In this talk, I will talk about a new technique named Stochastic Path-Integrated Differential EstimatoR (SPIDER), which can be used to track many deterministic quantities of interest with significantly reduced computational cost. We apply SPIDER to two tasks, namely the stochastic first-order and zeroth-order methods. For stochastic first-order method, combining SPIDER with normalized gradient descent, we propose two new algorithms, namely SPIDER-SFO and SPIDER-SFO+, that solve non-convex stochastic optimization problems using stochastic gradients only. We provide sharp error-bound results on their convergence rates. In special, we prove that the SPIDER-SFO and SPIDER-SFO+ algorithms achieve a record-breaking gradient computation cost of $O( \min( n^{1/2} \epsilon^{-2}, \epsilon^{-3} ) )$ for finding an \epsilon-approximate first-order and $O( \min( n^{1/2} \epsilon^{-2}+\epsilon^{-2.5}, \epsilon^{-3} ) )$ for finding an $(\epsilon, O(\epsilon^{0.5}))$-approximate second-order stationary point, respectively. In addition, we prove that SPIDER-SFO nearly matches the algorithmic lower bound for finding approximate first-order stationary points under the gradient Lipschitz assumption in the finite-sum setting. For stochastic zeroth-order method, we prove a cost of $O( d \min( n^{1/2} \epsilon^{-2}, \epsilon^{-3}) )$ which outperforms all existing results. Joint work with Cong Fang, Zhouchen Lin and Tong Zhang.​

报告摘要 Abstract

Dr. Junchi Li obtained his B.S. in Mathematics and Applied Mathematics at Peking University in 2009, and his Ph.D. in Mathematics at Duke University in 2014. He has since held several research positions, including the role of visiting postdoctoral research associate at Department of Operations Research and Financial Engineering, Princeton University. His research interests include statistical machine learning and optimization, scalable online algorithms for big data analytics, and stochastic dynamics on graphs and social networks. He has published original research articles in both top optimization journals and top machine learning conferences, including an oral presentation paper (1.23%) at NIPS 2017 and a spotlight paper (4.08%) at NIPS 2018.