BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Anima Anandkumar (Caltech) DTSTART:20200709T190000Z DTEND:20200709T203000Z DTSTAMP:20260423T021057Z UID:IASML/10 DESCRIPTION:Title: R ole of Interaction in Competitive Optimization\nby Anima Anandkumar (C altech) as part of IAS Seminar Series on Theoretical Machine Learning\n\n\ nAbstract\nCompetitive optimization is needed for many ML problems such as training GANs\, robust reinforcement learning\, and adversarial learning. Standard approaches to competitive optimization involve each agent indepe ndently optimizing their objective functions using SGD or other gradient-b ased approaches. However\, they suffer from oscillations and instability\, since the optimization does not account for interaction among the players . We introduce competitive gradient descent (CGD) that explicitly incorpor ates interaction by solving for Nash equilibrium of a local game. We exten d CGD to competitive mirror descent (CMD) for solving conically constraine d competitive problems by using the dual geometry induced by a Bregman div ergence.\n\nWe demonstrate the effectiveness of our approach for training GANs and solving constrained reinforcement learning (RL) problems. We also derive a competitive policy optimization method to train RL agents in com petitive games. Finally\, we provide a novel perspective on training GANs by pointing out the "GAN-dilemma" a fundamental flaw of the divergence-min imization perspective on GANs. Instead\, we argue that an implicit competi tive regularization due to simultaneous training methods\, such as CGD\, i s a crucial mechanism behind GAN performance.\n LOCATION:https://researchseminars.org/talk/IASML/10/ END:VEVENT END:VCALENDAR