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Tutte Colloquium - Katya ScheinbergExport this event to calendar

Friday, May 10, 2019 — 3:30 PM EDT

Title: Stochastic optimization methods beyond stochastic gradient descent

Speaker: Katya Scheinberg
Affiliation: Lehigh University
Room: MC 5501

Abstract:

We will present a very general framework for unconstrained stochastic optimization which encompasses standard frameworks such as line search and trust region using random models. In particular this framework retains the desirable practical features such step acceptance criterion, trust region adjustment and ability to utilize of second order models. The framework is based on bounding the expected stopping time of a stochastic process, which satisfies certain assumptions. Then the convergence rates are derived for each method by ensuring that the stochastic processes generated by the method satisfies these assumptions. The methods include a version of a stochastic trust-region method and a stochastic line-search methods that can be applied to both derivative based and derivative free optimization. Motivating examples from machine learning and reinforcement learning will be discussed.

Location 
MC - Mathematics & Computer Building
5501
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

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