Who am I?
- (July 2014) I just came back from my sabbatical that was split between Technion and MSR.
Thanks Shie and
Lihong for hosting me.
I am looking forward to working (more) closely again with the people in Alberta!
- (June 2014) With Nina Balcan,
I co-chaired COLT'14.
If you ask me, the program was exceptionally good! Although the work had to be done
during my sabbatical (mildly bothering), I think it was well worth doing it.
- (June 2014) Our ICML-14 paper
that connects Monte-Carlo estimation and bandit theory, joint
Dale was selected for the JMLR fast track.
As far as I know, only 18 out of the 1238 submissions were selected.
- (May 2014) Our paper
on resource allocation with bandit feedback, jointly authored with
won the best paper running up award at UAI'14.
- (January 2014) With Sandra,
I am the local organizer of ALT 2015, which will be held in beautiful
Banff in 2015 October. Mark your calendars!
- 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
should look here.
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
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:)