Sham Kakade talk at the NYU Tandon ECE Seminar Series on Modern AI: TOMORROW!!!

[Anna Choromanska]

Dear All,

Sham Kakade, professor in the Department of Computer Science and the Department of Statistics at the University of Washington and a senior principal researcher at Microsoft Research, will be with us on the 4th of May (TOMORROW!!!). His talk is scheduled at 11.00 am and you can use the following zoom to connect:

https://nyu.zoom.us/j/92428458815

NYU Tandon is looking forward to seeing you all!!!

Department of Electrical and Computer Engineering

Towards a Theory of Generalization in Reinforcement Learning

Time and Location May 4th, 2021 11 am Zoom Contact: ece-anno...@nyu.edu Zoom Link Sham Kakade University of Washington Sham Kakade is a professor in the Department of Computer Science and the Department of Statistics at the University of Washington and is also a senior principal researcher at Microsoft Research. He works on the mathematical foundations of machine learning and AI. Sham's thesis helped lay the statistical foundations of reinforcement learning. With his collaborators, his additional contributions include: one of the first provably efficient policy search methods in reinforcement learning; developing the mathematical foundations for the widely used linear bandit models and the Gaussian process bandit models; the tensor and spectral methodologies for provable estimation of latent variable models; the first sharp analysis of the perturbed gradient descent algorithm, along with the design and analysis of numerous other convex and non-convex algorithms. He is the recipient of the ICML Test of Time Award, the IBM Pat Goldberg best paper award, and INFORMS Revenue Management and Pricing Prize. He has been program chair for COLT 2011. Sham was an undergraduate at Caltech, where he studied physics and worked under the guidance of John Preskill in quantum computing. He completed his Ph.D. with Peter Dayan in computational neuroscience at the Gatsby Computational Neuroscience Unit. He was a postdoc with Michael Kearns at the University of Pennsylvania. Towards a Theory of Generalization in Reinforcement Learning A fundamental question in the theory of reinforcement learning is what properties govern our ability to generalize and avoid the curse of dimensionality. With regards to supervised learning, these questions are well understood theoretically, and, practically speaking, we have overwhelming evidence on the value of representational learning (say through modern deep networks) as a means for sample efficient learning. Providing an analogous theory for reinforcement learning is far more challenging, where even characterizing the representational conditions which support sample efficient generalization is far less well understood. This work will survey a number of recent advances towards characterizing when generalization is possible in reinforcement learning. We will start by reviewing this question in a simpler context, namely contextual bandits. Then we will move to lower bounds and consider one of the most fundamental questions in the theory of reinforcement learning, namely that of linear function approximation: suppose the optimal Q-function lies in the linear span of a given d dimensional feature mapping, is sample-efficient reinforcement learning (RL) possible? Finally, we will cover a new set of structural and representational conditions which permit generalization in reinforcement learning in a wide variety of settings through the use of function approximation.

This event is free and open to the public. The Seminar Series in Modern Artificial Intelligence is held at NYU Tandon School of Engineering and is hosted by the Department of Electrical and Computer Engineering. Organized by Professor Anna Choromanska, the series aims to bring together faculty and students to discuss the most important research trends in the world of AI. The speakers include world-renowned experts whose research is making an immense impact on the development of new machine learning techniques and technologies and helping to build a better, smarter, more-connected world.

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Anna Choromanska

Assistant Professor

Alfred P. Sloan Fellow

Department of Electrical and Computer Engineering

NYU Tandon School of Engineering

New York University

Room 802

370 Jay Street

New York, NY 11201, USA

Office phone: 646.997.0269

ac5455 at nyu dot edu

achoroma at gmail dot com

https://engineering.nyu.edu/faculty/anna-choromanska