The Hellenic Branch of Computational Intelligence of the International Institute of Electrical and Electronic Engineers (IEEE) and the Artificial Intelligence and Learning Systems Laboratory (AILS Lab) of the School of Electrical and Computer Engineering of the National Technical University of Athens invite you to the lecture of Prof. Christina Fragouli, IEEE Fellow, Department of Electrical & Computer Engineering, University of California, Los Angeles, USA.

on: “Solving Stochastic Contextual Bandits with Linear Bandits Algorithms”

The event will take place on Tuesday, October 15, 2024 at 1:00 p.m., in the Conference Hall of the School of Electrical and Computer Engineering, New Buildings, Zografou University of Technology.

The lecture will be given in English.

Abstract: Linear bandit and contextual linear bandit problems have recently attracted extensive attention as they enable to support impactful active learning applications through elegant formulations. In linear bandits, a learner at each round plays an action from a fixed action space and receives a reward that is specified by the inner product of the action and an unknown parameter vector plus noise. Contextual linear bandits add another layer of complexity by enabling at each round the action space to be different, to capture context. The goal is to design an algorithm that learns to play as close as possible to the unknown optimal policy after a number of action plays. The contextual problem is considered more challenging than the linear bandit problem, which can be viewed as a contextual bandit problem with a fixed context. Surprisingly, in this talk, we show that the stochastic contextual problem can be solved as if it is a linear bandit problem. In particular, we establish a novel reduction framework that converts every stochastic contextual linear bandit instance to a linear bandit instance. Our reduction framework opens up a new way to approach stochastic contextual linear bandit problems, and enables significant savings in communication cost in distributed setups. Furthermore, it yields improved regret bounds in a number of instances. This talk is based on joint work with Osama Hanna and Lin Yang.

Short CV: Christina Fragouli is a Professor in the Electrical and Computer Engineering Department at UCLA. She received the B.S. degree in Electrical Engineering from the National Technical University of Athens, Athens, Greece, and the M.Sc. and Ph.D. degrees in Electrical Engineering from the University of California, Los Angeles. She has worked at the Information Sciences Center, AT\&T Labs, Florham Park New Jersey, and also visited Bell Laboratories, Murray Hill, NJ, and DIMACS, Rutgers University. Between 2006--2015 she was an Assistant and Associate Professor in the School of Computer and Communication Sciences, EPFL, Switzerland. She is an IEEE fellow, she served as the 2022 President of the IEEE Information Theory Society (currently serving as Senior Past President), and has served in several IEEE-wide and Information Theory Society Committees as member or Chair. She has also served as TPC Chair in several conferences including the IEEE Information Theory Symposium in 20204, as an Information Theory Society Distinguished Lecturer, and as an Associate Editor for IEEE Communications Letters, for Elsevier Journal on Computer Communication, for IEEE Transactions on Communications, for IEEE Transactions on Information Theory, and for IEEE Transactions on Mobile Communications. She has received numerous awards including the Okawa Foundation Award, the European Research Council (ERC) Starting Investigator Grant, and the Zonta Price. Her research interests are in the intersection of coding techniques, machine learning and information theory, with a wide range of applications that include network information flow, network security and privacy, compression, wireless networks and bioinformatics.