Constantinos Daskalakis, Massachusetts Institute of Technology

Abstract

Despite impressive recent advances in multi-agent learning, developing analogues of the foundation models encountered in single-agent learning settings remains elusive. The challenge is multi-faceted, and this talk will focus on one of the many facets: the lack of a clear training target. This derives from the fact that multi-agent settings involving agents who use DNNs to model their strategies or utilities quickly escape the scope of classical Game Theory—due to non-convexities, Nash equilibrium existence breaks and standard equilibrium analysis is inapplicable. If this is the case, what is even the goal of training a model in such a setting? I will discuss this challenge and how to overcome it from a combined game-theoretic, complexity-theoretic and learning-theoretic perspective.

[Video]

Speaker bio: Constantinos (aka "Costis") Daskalakis is the Avanessians Professor of Electrical Engineering and Computer Science at MIT. He holds a Diploma in Electrical and Computer Engineering from the National Technical University of Athens, and a PhD in Electrical Engineering and Computer Science from UC Berkeley. He works on Computation Theory and its interface with Game Theory, Economics, Probability Theory, Machine Learning and Statistics. He has resolved long-standing open problems about the computational complexity of Nash equilibrium, and the mathematical structure and computational complexity of multi-item auctions. His current work focuses on multi-agent learning, high-dimensional statistics, learning from biased and dependent data, causal inference and econometrics. He has been honored with the ACM Doctoral Dissertation Award, the Kalai Prize from the Game Theory Society, the Sloan Fellowship in Computer Science, the SIAM Outstanding Paper Prize, the Microsoft Research Faculty Fellowship, the Simons Investigator Award, the Rolf Nevanlinna Prize from the International Mathematical Union, the ACM Grace Murray Hopper Award, the Bodossaki Foundation Distinguished Young Scientists Award, the ACM SIGECOM Test of Time Award, and the FOCS 2022 Test of Time Award. He is an ACM fellow.