University of California at Santa Cruz
July 6-9, 1999
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A PRELIMINARY PROGRAM
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Tuesday, July 6
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Session 1 (9:00-10:30)
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The Robustness of the p-norm Algorithms, Claudio Gentile and Nick
Littlestone
Minimax Regret under Log Loss for General Classes of Experts,
Nicolo Cesa-Bianchi and Gabor Lugosi
On Prediction of Individual Sequences Relative to a set of Experts,
Neri Merhav and Tsachy Weissman
Regret Bounds for Prediction Problems, Geoffrey J. Gordon
Session 2 (11:00-12:00)
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On theory revision with queries, Robert H. Sloan and Gyorgy Turan
Estimating a mixture of two product distributions, Yoav Freund and
Yishay Mansour
An Apprentice Learning Model, Stephen S. Kwek
Session 3 (2:00-3:00)
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Uniform-Distribution Attribute Noise Learnability, Nader H. Bshouty and
Jeffrey C. Jackson and Christino Tamon
On Learning in the Presence of Unspecified Attribute Values, Nader
H. Bshouty and David K. Wilson
Learning Fixed-dimension Linear Thresholds From Fragmented Data,
Paul W. Goldberg
Tutorial 1 (3:30-5:30)
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Boosting, Yoav Freund and Rob Schapire
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19:00 - 21:00 RECEPTION
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Wednesday, July 7
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Invited Speaker
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TBA, David Shmoys (9:00-10:00)
Session 4 (10:30 - 12:10)
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An adaptive version of the boost-by-majority algorithm, Yoav Freund
Drifting Games, Robert E. Schapire
Additive Models, Boosting, and Inference for Generalized Divergences,
John Lafferty
Boosting as Entropy Projection, J. Kivinen and M. K. Warmuth
Multiclass Learning, Boosting, and Error-Correcting Codes, Venkatesan
Guruswami and Amit Sahai
Session 5 (2:00-3:00)
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Theoretical Analysis of a Class of Randomized Regularization Methods,
Tong Zhang
PAC-Bayesian Model Averaging, David McAllester
Viewing all Models as `Probabilistic', Peter Grunwald
Tutorial 2 (3:30- 5:30)
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Reinforcement Learning, Michael Kearns (?) and Yishay Mansour
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Thursday, July 8
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Session 6 (9-10:30)
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Reinforcement Learning and Mistake Bounded Algorithms, Yishay
Mansour
Convergence analysis of temporal-difference learning algorithms,
Vladislav Tadic
Beating the Hold-Out, Avrim Blum and Adam Kalai and John Langford
Microchoice Bounds and Self Bounding Learning Algorithms, John
Langford and Avrim Blum
Session 7 (11:00- 12:00)
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Learning Specialist Decision Lists, Atsuyoshi Nakamura
Linear Relations between Square-Loss and Kolmogorov Complexity,
Yuri A. Kalnishkan
Individual sequence prediction - upper bounds and application for
complexity, Chamy Allenberg
Session 8 (2:00- 3:00)
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Extensional Set Learning, S. A. Terwijn
On a generalized notion of mistake bounds, Sanjay Jain and Arun
Sharma
On the intrinsic complexity of learning infinite objects from finite
samples, Kinber and Papazian and Smith and Wiehagen
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Friday, July 9
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Tutorial 3 (9:00-11:00)
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Large Margin Classification, Peter Bartlett, John Shawe-Taylor,
and Bob Williamson
Session 9 (11:30-12:10)
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Covering Numbers for Support Vector Machines, Ying Guo and Peter
L. Bartlett and John Shawe-Taylor and Robert C. Williamson
Further Results on the Margin Distribution, John Shawe-Taylor and
Nello Cristianini
Session 10 (2:00- 3:40)
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Attribute Efficient PAC-learning of DNF with Membership Queries,
Nader H. Bshouty and Jeffrey C. Jackson and Christino Tamon
On PAC Learning Using Winnow, Perceptron, and a Perceptron-Like
Algorithm, Rocco A. Servedio
Extension of the PAC Framework to Finite and Countable Markov Chains,
David Gamarnik
Learning threshold functions with small weights using membership
queries., E. Abboud, N. Agha, N.H. Bshouty, N. Radwan, F. Saleh
Exact Learning of Unordered Tree Patterns From Queries, Thomas R.
Amoth and Paul Cull and Prasad Tadepalli
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