Who is this guy?
- (August 16, 2016) Bandits:
My new graduate course
co-developed with Tor.
- (August 15, 2016) Two out of two submitted papers accepted at NIPS!
Details coming soon.
- Prospective grad students
who are interested in joining
the Statistical Machine Learning
degree specialization program, which is a joint program between
our department and the MathStat department
Why should you apply?
- Here is some advice for present and future grad students.
- Responding to an "emergency situation", back in 2008
I have spent a few hours by searching on the IEEE website to collect recent references on
applications of RL.
are the results which are now linked to the page on
Successes of RL.
See also Satinder's similarly titled page
Online learning research develops of learning algorithms that show good
online performance, i.e., good performance while learning. Online
learning tasks are sequential: In each step of the sequential process,
the learning algorithm receives some information from the environment
and makes a prediction so as to minimize the prediction loss. My team
and I focus on interactive online learning problems, sequential
processes where the predictions influence what future information
is received. Interactive online learning problems are studied in
various disciplines, such as within control theory under the name
"dual control", or within machine learning itself in the area of
reinforcement learning. While these problems are natural, interactive
online learning is perhaps the area that is the least developed within
online learning. To make progress, we explore special cases of
interactive online learning, which allows us to identify and study
the key issues in isolation. Besides, developing better algorithms
for these special cases is of independent interest as they have
often interesting uses on their own. We also study more fundamental
questions as they arise.
Big picture: I am interested in machine learning
In particular, I like to think about how to make the most efficient
use of data in various situations and also
how this can be done algorithmically
I am particularly interested in sequential decision making problems
, which, when learning is put into the picture, leads to reinforcement learning
Up to 2008, the most frequently occuring keywords associated with my publications
reinforcement learning (49),
neural networks (24),
stochastic approximation (17),
function approximation (16),
online learning (13),
adaptive control (10),
performance bounds (10),
Monte-Carlo methods (8),
particle filtering (8)
There is a fair amount of noise in the numbers here. And the chronology is also somewhat important. For example, I focused on neural networks
up to around 2001:)