From shai@cs.Technion.AC.IL Tue May 11 17:01:53 1999 Date: Tue, 11 May 1999 15:42:07 PDT From: Shai Ben-David Reply-To: Theory-A - TheoryNet World-Wide Events , Shai Ben-David To: THEORYNT@LISTSERV.NODAK.EDU Subject: COLT99 program Twelfth Annual Conference on Computational Learning Theory University of California at Santa Cruz July 6-9, 1999 ======================================== A PRELIMINARY PROGRAM ======================================== Tuesday, July 6 --------------- Session 1 (9:00-10:30) --------- 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) --------- 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 ++++++++++++++++++++++++++++++++++++++++ 19:00 - 21:00 RECEPTION +++++++++++++++++++++++++++++++++++++++++ Wednesday, July 7 ----------------- Invited Speaker --------------- TBA, David Shmoys (9:00-10:00) Session 4 (10:30 - 12:10) --------- 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) --------- 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 +++++++++++++++++++++++++++++++++++++++++ -------------- Thursday, July 8 ----------------- Session 6 (9-10:30) --------- 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) --------- 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) ---------- 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 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Friday, July 9 -------------- Tutorial 3 (9:00-11:00) ---------- Large Margin Classification, Peter Bartlett, John Shawe-Taylor, and Bob Williamson Session 9 (11:30-12:10) --------- 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) ---------- 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 +++++++++++++++++++++++++++++++++++++++++