Machine learning algorithms are increasingly being deployed into environments in which they must interact with other strategic agents with potentially misaligned objectives. The presence of these other agents breaks many of the underlying assumptions on which machine learning algorithms are built. In particular, they can cause non-stationarity in the environment that gives rise to surprising dynamics and behaviors. In this talk, we will explore the challenges and opportunities available to algorithm designers in such scenarios and show how one can take advantage of the game theoretic interactions in the environment to give performance and convergence guarantees to game theoretically meaningful solutions.
Eric Mazumdar is an Assistant Professor in Computing and Mathematical Sciences and Economics at Caltech. He is the recipient of an NSF Career Award and a Simons-Berkeley Fellowship from the Simons Institute. Eric obtained his Ph.D in Electrical Engineering and Computer Science at UC Berkeley where he was advised by Michael Jordan and Shankar Sastry. Prior to Berkeley, he received a B.S. in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT).