Ομιλία Χριστίνας Φραγκούλη:
Μέρα/Ώρα: Τετάρτη 26/2, ώρα 3μμ
Τόπος: Τ106, κτήριο Τροίας
MS Teams Link:
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.
Bio:
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.
