Feb 20th, 2018, 12:00pm
Walid Krichene, Google
Abstract: Many optimization algorithms can be viewed as a discretization of a continuous-time process (described using an ordinary differential equation, or a stochastic differential equation in the presence of noise). The continuous-time point of view can be useful for simplifying the analysis, drawing connections with physics, and streamlining the design of new algorithms and heuristics. We will review results from continuous-time optimization, from the simple case of gradient flow, to more recent results on accelerated methods. In particular, we give simple interpretations of acceleration, and show how these interpretations motivate heuristics (restarting and adaptive averaging) which, empirically, can significantly improve the speed of convergence. We will then focus on the stochastic case, and study the interaction between acceleration and noise, and their effect on the convergence rates. We will conclude with a brief review of how the same tools can be applied in other problems, such as approximate sampling and non-convex optimization.
Bio: Walid Krichene is at Google Research, where he works on large-scale optimization and recommendation. He received his Ph.D. in EECS in 2016 from UC Berkeley, where he was advised by Alex Bayen and Peter Bartlett, a M.A. in Mathematics from U.C. Berkeley, and a M.S. in Engineering and Applied Math from the Ecole des Mines ParisTech. He received the Leon Chua Award and two outstanding GSI awards from U.C. Berkeley. His research interests include convex optimization, stochastic approximation, recommender systems, and online learning.