UTCS Colloquia /AI - Csaba Szepesvari/University of Alberta: "How to choose cakes (if you must?) -- advice from statistics", ACES 2.402
There is a sign-up schedule for this event that can be found at
Type of Talk: UTCS Colloquia /AI
Szepesvari/University of Alberta
Date/Time: Wednesday, November 10,
2010, 11:00 a.m.
Location: ACES 2.402
Host: Peter Stone
lk Title: "How to choose cakes (if you must?) -- advice from statistics"
Every time you visit a new town you go to its best conf
ectionery. Which cake to choose? Needless to say cakes are made a little di
fferently in every town. Should you choose the familiar favorite of yours o
r should you try a new one so that you are not missing something very good?
How to choose if there are a very large number of cakes, maybe more than
days in your life? Or even infinite? Of course, this problem is an instanc
e of the classic multi-armed bandit problem.
Applications range from p
roject management, pricing products, through calibration of physical proc
esses, monitoring and control of wireless networks, to optimizing website
content. In this talk I will describe some recent results about when the s
pace of options is very large or even infinite with more or less structure.
I will outline several open problems with varying difficulty. The talk is
based on joint work with (in chronological order) Peter Auer, Ronald Ortne
r (Graz, Austria), Yasin Abbasi-Yadkori (UofA), Sarah Filippi, Olivier
Cappe, Aurilien Garivier (Telecom ParisTech, CNRS), and Pallavi Arora an
d Rong Zheng (University of Houston, TX).
vari received his PhD in 1999 from University, Szeged, Hungary. He is cur
rently an Associate Professor at the Department of Computing Science of the
University of Alberta and a principal investigator of the Alberta Ingenuit
y Center for Machine Learning. Previously, he held a senior researcher pos
ition at the Computer and Automation Research Institute of the Hungarian Ac
ademy of Sciences, where he headed the Machine Learning Group. Before that
, he spent 5 years in the software industry. In 1998, he became the Resea
rch Director of Mindmaker, Ltd., working on natural language processing a
nd speech products, while from 2000, he became the Vice President of Rese
arch at the Silicon Valley company Mindmaker Inc. He is the coauthor of a b
ook on nonlinear approximate adaptive controllers, a recent short book on
Reinforcement Learning, published over 100 journal and conference papers.
He serves as the Associate Editor of IEEE Transactions on Adaptive Control
and AI Communications, is on the board of editors of the Journal of Machin
e Learning Research and the Machine Learning Journal, and is a regular mem
ber of the program committee at various machine learning and AI conferences
. His areas of expertise include statistical learning theory, reinforcemen
t learning and nonlinear adaptive control.
- Awards & Honors
- About Us
- Student Engagement and Support
- Masters Program
- Ph.D. Program
- Financial Information
- Prospective Students
- Incoming Students
- Current Students
- Curricular Practical Training
- Grad Student Talks
- UTCS Direct