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ORCID16th COLT 2003: Washington, DC, USA
- Bernhard Schölkopf, Manfred K. Warmuth:
Computational Learning Theory and Kernel Machines, 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, Proceedings. Lecture Notes in Computer Science 2777, Springer 2003, ISBN 3-540-40720-0
Target Area: Computational Game Theory
- Michael L. Littman:
Tutorial: Learning Topics in Game-Theoretic Decision Making. 1
Invited Talk
- Amy Greenwald, Amir Jafari:
A General Class of No-Regret Learning Algorithms and Game-Theoretic Equilibria. 2-12
Contributed Talks
- Avrim Blum, Jeffrey C. Jackson, Tuomas Sandholm, Martin Zinkevich:
Preference Elicitation and Query Learning. 13-25 - Adam Kalai, Santosh S. Vempala:
Efficient Algorithms for Online Decision Problems. 26-40
Kernel Machines
- Corinna Cortes, Patrick Haffner, Mehryar Mohri:
Positive Definite Rational Kernels. 41-56 - Tony Jebara, Risi Kondor:
Bhattacharyya Expected Likelihood Kernels. 57-71 - Matthias Hein, Olivier Bousquet:
Maximal Margin Classification for Metric Spaces. 72-86 - Roni Khardon, Rocco A. Servedio:
Maximum Margin Algorithms with Boolean Kernels. 87-101 - Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik:
Knowledge-Based Nonlinear Kernel Classifiers. 102-113 - Christina S. Leslie, Rui Kuang:
Fast Kernels for Inexact String Matching. 114-128 - Thomas Gärtner
, Peter A. Flach, Stefan Wrobel:
On Graph Kernels: Hardness Results and Efficient Alternatives. 129-143 - Alexander J. Smola, Risi Kondor:
Kernels and Regularization on Graphs. 144-158 - Ilya Desyatnikov, Ron Meir:
Data-Dependent Bounds for Multi-category Classification Based on Convex Losses. 159-172
Poster Session 1
- Marina Meila:
Comparing Clusterings by the Variation of Information. 173-187 - Fei Sha, Lawrence K. Saul, Daniel D. Lee:
Multiplicative Updates for Large Margin Classifiers. 188-202 - David A. McAllester:
Simplified PAC-Bayesian Margin Bounds. 203-215 - Michinari Momma, Kristin P. Bennett:
Sparse Kernel Partial Least Squares Regression. 216-230 - Shantanu Chakrabartty, Gert Cauwenberghs, Jayadeva:
Sparse Probability Regression by Label Partitioning. 231-242 - Jinbo Bi, Vladimir Vapnik:
Learning with Rigorous Support Vector Machines. 243-257 - Balázs Kégl:
Robust Regression by Boosting the Median. 258-272 - Sanjoy Dasgupta, Philip M. Long:
Boosting with Diverse Base Classifiers. 273-287 - Jaz S. Kandola, Thore Graepel, John Shawe-Taylor:
Reducing Kernel Matrix Diagonal Dominance Using Semi-definite Programming. 288-302
Statistical Learning Theory
- Alexandre B. Tsybakov:
Optimal Rates of Aggregation. 303-313 - Ulrike von Luxburg, Olivier Bousquet:
Distance-Based Classification with Lipschitz Functions. 314-328 - Shahar Mendelson, Petra Philips:
Random Subclass Bounds. 329-343 - Avrim Blum, John Langford:
PAC-MDL Bounds. 344-357
Online Learning
- Vladimir Vovk:
Universal Well-Calibrated Algorithm for On-Line Classification. 358-372 - Nicolò Cesa-Bianchi, Alex Conconi, Claudio Gentile:
Learning Probabilistic Linear-Threshold Classifiers via Selective Sampling. 373-387 - Koby Crammer, Yoram Singer:
Learning Algorithm for Enclosing Points in Bregmanian Spheres. 388-402 - Gilles Stoltz, Gábor Lugosi:
Internal Regret in On-Line Portfolio Selection. 403-417
Other Approaches
- Shie Mannor, John N. Tsitsiklis:
Lower Bounds on the Sample Complexity of Exploration in the Multi-armed Bandit Problem. 418-432 - Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer:
Smooth e-Intensive Regression by Loss Symmetrization. 433-447 - Nina Mishra, Dana Ron, Ram Swaminathan:
On Finding Large Conjunctive Clusters. 448-462 - Adam R. Klivans, Amir Shpilka:
Learning Arithmetic Circuits via Partial Derivatives. 463-476
Poster Session 2
- Malik Magdon-Ismail, Joseph Sill:
Using a Linear Fit to Determine Monotonicity Directions. 477-491 - Vladimir Koltchinskii, Dmitry Panchenko, Savina Andonova:
Generalization Bounds for Voting Classifiers Based on Sparsity and Clustering. 492-505 - Marcus Hutter:
Sequence Prediction Based on Monotone Complexity. 506-521 - Yuri Kalnishkan, Vladimir Vovk, Michael V. Vyugin:
How Many Strings Are Easy to Predict? 522-536 - Marta Arias, Roni Khardon, Rocco A. Servedio:
Polynomial Certificates for Propositional Classes. 537-551 - Shie Mannor, Nahum Shimkin:
On-Line Learning with Imperfect Monitoring. 552-566 - Shai Ben-David, Reba Schuller:
Exploiting Task Relatedness for Mulitple Task Learning. 567-580 - Eyal Even-Dar, Yishay Mansour:
Approximate Equivalence of Markov Decision Processes. 581-594 - Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
An Information Theoretic Tradeoff between Complexity and Accuracy. 595-609 - Jeffrey C. Jackson, Rocco A. Servedio:
Learning Random Log-Depth Decision Trees under the Uniform Distribution. 610-624 - Robert H. Sloan, Balázs Szörényi, György Turán:
Projective DNF Formulae and Their Revision. 625-639 - Aharon Bar-Hillel, Daphna Weinshall:
Learning with Equivalence Constraints and the Relation to Multiclass Learning. 640-654
Target Area: Natural Language Processing
- Michael Collins:
Tutorial: Machine Learning Methods in Natural Language Processing. 655
Invited Talks
- Mehryar Mohri:
Learning from Uncertain Data. 656-670 - Mark Johnson:
Learning and Parsing Stochastic Unification-Based Grammars. 671-683
Inductive Inference Learning
- John Case, Keh-Jiann Chen, Sanjay Jain, Wolfgang Merkle, James S. Royer:
Generality's Price: Inescapable Deficiencies in Machine-Learned Programs. 684-698 - John Case, Sanjay Jain, Franco Montagna, Giulia Simi, Andrea Sorbi:
On Learning to Coordinate: Random Bits Help, Insightful Normal Forms, and Competency Isomorphisms. 699-713 - Sanjay Jain, Efim B. Kinber, Rolf Wiehagen:
Learning All Subfunctions of a Function. 714-728
Open Problems
- Amiran Ambroladze, John Shawe-Taylor:
When Is Small Beautiful? 729-730 - Avrim Blum:
Learning a Function of r Relevant Variables. 731-733 - Sanjoy Dasgupta:
Subspace Detection: A Robust Statistics Formulation. 734 - Sanjoy Dasgupta:
How Fast Is k-Means? 735 - Yoav Freund, Alon Orlitsky, Prasad Santhanam, Junan Zhang:
Universal Coding of Zipf Distributions. 736-737 - Marcus Hutter:
An Open Problem Regarding the Convergence of Universal A Priori Probability. 738-740 - Vladimir Koltchinskii:
Entropy Bounds for Restricted Convex Hulls. 741-742 - Manfred K. Warmuth:
Compressing to VC Dimension Many Points. 743-744
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