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Talk Title: Machine Learning in Dynamical Systems

Talk Abstract: Many branches of science and engineering involve estimation and control in dynamical systems; consider, for example, using data to help stabilize the flight of a drone or predict the path of a hurricane. We consider control in dynamical systems from the perspective of regret minimization. Unlike most prior work in this area, we focus on the problem of designing an online controller which competes with the best dynamic sequence of control actions selected in hindsight, instead of the best controller in some specific class of controllers. This formulation is attractive when the environment changes over time and no single controller achieves good performance over the entire time horizon. We derive the structure of the regret-optimal online controller using techniques from robust control theory and present a clean data-dependent bound on its regret. We also present numerical simulations which confirm that our regret-optimal controller significantly outperforms various classical controllers in dynamic environments.

Bio: Gautam is a PhD student in the Computing and Mathematical Sciences (CMS) department at Caltech, where he is advised by Babak Hassibi. He is broadly interested in machine learning, optimization, and control, especially 1) online learning and online decision-making and 2) integrating machine learning with physics, dynamics and control. Much of his PhD work has been supported by a National Science Foundation Graduate Research Fellowship and an Amazon AWS AI Fellowship. Prior to joining Caltech, he obtained a BS in Mathematics from Georgia Tech.