ICML / COLT 98 Overview
A number of us were fortunate enough to be able to attend some of the
many cool AI conferences that were co-located in Madison, WI this
summer. In this meeting we will try to review some of what went on at
the ICML and COLT conferences.
The meeting will be organized as follows: We will start with a general
open discussion of the big trends and the hot topics. Then a number
of people will be making a series of short presentations about papers
they found interesting:
Andrew Moore - "Reinforcement Learning: How Far Can It Go?"
by Rich Sutton
envy.cs.umass.edu/~rich/ICML-98/index.htm
Sean Slattery - "The Case Against Accuracy Estimation for Comparing
Induction Algorithms" by Provost, Fawcett, and Kohavi
www.croftj.net/~fawcett/papers/ICML98-final.ps.gz
Kamal Nigam - "Combining Nearest Neighbor Classifiers through
Multiple Feature Subsets" by Bay
www.ics.uci.edu/~sbay/papers/mfs_icml98.ps
Chuck Rosenberg - "An Efficient Boosting Algorithm for Combining
Preferences" by Freund, Iyer, Schapire and, Singer
www.research.att.com/~schapire/papers/FreundIyScSi98.ps.Z
For more information about the talks given at ICML-98:
http://www.cs.wisc.edu/icml98/schedule.html
For more information about COLT-98:
http://theory.lcs.mit.edu/COLT-98/