Thu, 5-May-2022 / 4:00pm / Packard 101
In this talk we will discuss an emerging paradigm in differentiable reinforcement learning called “nonstochastic control”. The new approach applies techniques from online convex optimization and convex relaxations to obtain new methods with provable guarantees for classical settings in optimal and robust control. We will discuss recent extensions to nonlinear adaptive control and planning.
No background is required for this talk, and relevant materials can be found here.
Elad Hazan is a professor of computer science at Princeton University. His research focuses on the design and analysis of algorithms for basic problems in machine learning and optimization. Amongst his contributions are the co-invention of the AdaGrad algorithm for deep learning, and the first sublinear-time algorithms for convex optimization. He is the recipient of the Bell Labs prize, the IBM Goldberg best paper award twice, in 2012 and 2008, a European Research Council grant, a Marie Curie fellowship, twice the Google Research Award and ACM fellowship. He served on the steering committee of the Association for Computational Learning and has been program chair for COLT 2015. He is the co-founder and director of Google AI Princeton.
Packard Building
Electrical Engineering Department
350 Jane Stanford Way
Stanford, CA 94305-9515
Thursdays, 4pm, Packard 202
