[talks] I Mukherjee general exam

Melissa M Lawson mml at CS.Princeton.EDU
Mon May 12 09:17:11 EDT 2008


Indraneel Mukherjee will present his research seminar/general exam on Friday May 16 
at 2PM in Room 402.  The members of his committee are: Rob Schapire (advisor), 
David Blei, and Ken Steiglitz.  Everyone is invited to attend his talk, and those faculty 
wishing to remain for the oral exam following are welcome to do so.  His abstract and 
reading list follow below.
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Abstract:
=========

We consider the problem of learning to predict as well as the best in a group of experts
making continuous predictions. We assume the learning algorithm has prior knowledge of the
maximum number of mistakes of the best expert. We propose a new master strategy that
achieves the best known performance for online learning with continuous experts in the
mistake bounded model. Our ideas are based on drifting games, a generalization of boosting
and online learning algorithms.
We also prove new lower bounds based on the drifting games framework which, though not as
tight as previous bounds, have simpler proofs and do not require an enormous number of
experts.

Papers:
=======

1. Yoav Freund and Manfred Opper.
    Drifting games and Brownian motion.
    Journal of Computer and System Sciences, 64:113--132, 2002.
    A preliminary version appeared in the Proceedings of the 13th
    Annual Conference on Computational Learning Theory, 2000.

2. Yoav Freund.
    An adaptive version of the boost by majority algorithm.
    Machine Learning, 43(3):293--318, June 2001.
    A preliminary version appeared in the Proceedings of the 12th
    Annual Conference on Computational Learning Theory, 1999.

3. Yoav Freund.
    Boosting a weak learning algorithm by majority.
    Information and Computation, 121(2):256--285, 1995.
    Preliminary versions appeared in the Proceedings of the Third
    Annual Workshop on Computational Learning Theory, 1990, and in the
    Proceedings of the Fifth Annual ACM Workshop on Computational
    Learning Theory, 1992.

4. Robert E. Schapire.
    Drifting games.
    Machine Learning, 43(3):265-291, 2001.

5. N. Cesa-Bianchi, Y. Freund, D.P. Helmbold, D. Haussler,
    R. Schapire, and M.K. Warmuth
    How to use expert advice
    Journal of the ACM, 44(3):427-485, 1997.

6. N. Cesa-Bianchi, Y. Freund, D.P. Helmbold, and M.K. Warmuth
    On-line prediction and conversion strategies
    Machine Learning, 25:71-110, 1996.

7. J. Abernethy, J. Langford, M. Warmuth
    Continuous Experts and the Binning Algorithm
    COLT 2006

8. Nick Littlestone and Manfred K. Warmuth
    The weighted majority algorithm.
    Information and Computation, 108:212-261, 1994.

9. Joel Spencer
    Ulam's searching game with a fixed number of lies.
    Theoretical Computer Science, 95(2):307-321, 1992

10. David Blackwell
     An analog of the minmax theorem for vector payoffs.
     Pacific J. Math. Volume 6, Number 1 (1956), 1-8.

Books:
======

1. Stuart Russell, Peter Norvig.
    Artificial Intelligence: A modern approach.
    Chapters:
      3.1-3.5
      4.1-4.3
      6
      7.1-7.6
      11.1-11.5
      8.1-8.3
      13.1-13.6
      14.1-14.5
      15.1-15.6
      16.1-16.3
      17.1-17.4
      18.1-18.5
      20.1-20.3,20.5-20.6
      21.1-21.4
      26


2. Luc Devroye, Laszlo Gyorfi, Gabor Lugosi
    A Probabilistic Theory of Pattern Recognition
    Chapters:
      2,4,6.1,6.2,6.7,6.8
      7,8,12,13,14,15,16,
      28, 29, 30

3. Nicolo Cesa Bianchi, Gabor Lugosi
    Prediction Learning and Games
    Chapters:
      2,3,
      4.1-4.3,
      6.1-6.3,6.7-6.9,
      7.1-7.3, 7.7 - 7.8,
      8,
      9.1-9.5, 9.8 - 9.11,
      all of 10 except 10.6,
      11.1 - 11.5



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