COLT99 program

[Shai Ben-David]

Twelfth Annual Conference on
Computational Learning Theory


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)
---------
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)
---------
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)
----------
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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