Equilibrium Complexity and Deep Learning
Abstract
Deep Learning has recently made significant progress in learning challenges such as speech and image recognition, automatic translation, and text generation, much of that progress being fueled by the success of gradient descent-based optimization methods in computing local optima of non-convex objectives. From robustifying machine learning models against adversarial attacks to causal inference, training generative models, multi-robot interactions, and learning in strategic environments, many outstanding challenges in Machine Learning lie at its interface with Game Theory. On this front, however, Deep Learning has been less successful. Here, the role of single-objective optimization is played by equilibrium computation, but gradient-descent based methods fail to find equilibria, and even computing local equilibria—the analog of computing local optima in single-agent settings—has remained elusive.
Bio
Constantinos "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.
