Table of Contents

• Tutorial: Learning Topics in Game-Theoretic Decision MakingMichael L. Littman. 1 [doi]
• A General Class of No-Regret Learning Algorithms and Game-Theoretic EquilibriaAmy R. Greenwald, Amir Jafari. 2-12 [doi]
• Preference Elicitation and Query LearningAvrim Blum, Jeffrey C. Jackson, Tuomas Sandholm, Martin Zinkevich. 13-25 [doi]
• Efficient Algorithms for Online Decision ProblemsAdam Kalai, Santosh Vempala. 26-40 [doi]
• Positive Definite Rational KernelsCorinna Cortes, Patrick Haffner, Mehryar Mohri. 41-56 [doi]
• Bhattacharyya Expected Likelihood KernelsTony Jebara, Risi Imre Kondor. 57-71 [doi]
• Maximal Margin Classification for Metric SpacesMatthias Hein, Olivier Bousquet. 72-86 [doi]
• Maximum Margin Algorithms with Boolean KernelsRoni Khardon, Rocco A. Servedio. 87-101 [doi]
• Knowledge-Based Nonlinear Kernel ClassifiersGlenn Fung, Olvi L. Mangasarian, Jude W. Shavlik. 102-113 [doi]
• Fast Kernels for Inexact String MatchingChristina S. Leslie, Rui Kuang. 114-128 [doi]
• On Graph Kernels: Hardness Results and Efficient AlternativesThomas Gärtner, Peter A. Flach, Stefan Wrobel. 129-143 [doi]
• Kernels and Regularization on GraphsAlex J. Smola, Risi Imre Kondor. 144-158 [doi]
• Data-Dependent Bounds for Multi-category Classification Based on Convex LossesIlya Desyatnikov, Ron Meir. 159-172 [doi]
• Comparing Clusterings by the Variation of InformationMarina Meila. 173-187 [doi]
• Multiplicative Updates for Large Margin ClassifiersFei Sha, Lawrence K. Saul, Daniel D. Lee. 188-202 [doi]
• Simplified PAC-Bayesian Margin BoundsDavid A. McAllester. 203-215 [doi]
• Sparse Kernel Partial Least Squares RegressionMichinari Momma, Kristin P. Bennett. 216-230 [doi]
• Sparse Probability Regression by Label PartitioningShantanu Chakrabartty, Gert Cauwenberghs, Jayadeva. 231-242 [doi]
• Learning with Rigorous Support Vector MachinesJinbo Bi, Vladimir Vapnik. 243-257 [doi]
• Robust Regression by Boosting the MedianBalázs Kégl. 258-272 [doi]
• Boosting with Diverse Base ClassifiersSanjoy Dasgupta, Philip M. Long. 273-287 [doi]
• Reducing Kernel Matrix Diagonal Dominance Using Semi-definite ProgrammingJaz S. Kandola, Thore Graepel, John Shawe-Taylor. 288-302 [doi]
• Optimal Rates of AggregationAlexandre B. Tsybakov. 303-313 [doi]
• Distance-Based Classification with Lipschitz FunctionsUlrike von Luxburg, Olivier Bousquet. 314-328 [doi]
• Random Subclass BoundsShahar Mendelson, Petra Philips. 329-343 [doi]
• PAC-MDL BoundsAvrim Blum, John Langford. 344-357 [doi]
• Universal Well-Calibrated Algorithm for On-Line ClassificationVladimir Vovk. 358-372 [doi]
• Learning Probabilistic Linear-Threshold Classifiers via Selective SamplingNicolò Cesa-Bianchi, Alex Conconi, Claudio Gentile. 373-387 [doi]
• Learning Algorithm for Enclosing Points in Bregmanian SpheresKoby Crammer, Yoram Singer. 388-402 [doi]
• Internal Regret in On-Line Portfolio SelectionGilles Stoltz, Gábor Lugosi. 403-417 [doi]
• Lower Bounds on the Sample Complexity of Exploration in the Multi-armed Bandit ProblemShie Mannor, John N. Tsitsiklis. 418-432 [doi]
• Smooth e-Intensive Regression by Loss SymmetrizationOfer Dekel, Shai Shalev-Shwartz, Yoram Singer. 433-447 [doi]
• On Finding Large Conjunctive ClustersNina Mishra, Dana Ron, Ram Swaminathan. 448-462 [doi]
• Learning Arithmetic Circuits via Partial DerivativesAdam Klivans, Amir Shpilka. 463-476 [doi]
• Using a Linear Fit to Determine Monotonicity DirectionsMalik Magdon-Ismail, Joseph Sill. 477-491 [doi]
• Generalization Bounds for Voting Classifiers Based on Sparsity and ClusteringVladimir Koltchinskii, Dmitry Panchenko, Savina Andonova. 492-505 [doi]
• Sequence Prediction Based on Monotone ComplexityMarcus Hutter. 506-521 [doi]
• How Many Strings Are Easy to Predict?Yuri Kalnishkan, Vladimir Vovk, Michael V. Vyugin. 522-536 [doi]
• Polynomial Certificates for Propositional ClassesMarta Arias, Roni Khardon, Rocco A. Servedio. 537-551 [doi]
• On-Line Learning with Imperfect MonitoringShie Mannor, Nahum Shimkin. 552-566 [doi]
• Exploiting Task Relatedness for Mulitple Task LearningShai Ben-David, Reba Schuller. 567-580 [doi]
• Approximate Equivalence of Markov Decision ProcessesEyal Even-Dar, Yishay Mansour. 581-594 [doi]
• An Information Theoretic Tradeoff between Complexity and AccuracyRan Gilad-Bachrach, Amir Navot, Naftali Tishby. 595-609 [doi]
• Learning Random Log-Depth Decision Trees under the Uniform DistributionJeffrey C. Jackson, Rocco A. Servedio. 610-624 [doi]
• Projective DNF Formulae and Their RevisionRobert H. Sloan, Balázs Szörényi, György Turán. 625-639 [doi]
• Learning with Equivalence Constraints and the Relation to Multiclass LearningAharon Bar-Hillel, Daphna Weinshall. 640-654 [doi]
• Tutorial: Machine Learning Methods in Natural Language ProcessingMichael Collins. 655 [doi]
• Learning from Uncertain DataMehryar Mohri. 656-670 [doi]
• Learning and Parsing Stochastic Unification-Based GrammarsMark Johnson. 671-683 [doi]
• Generality s Price: Inescapable Deficiencies in Machine-Learned ProgramsJohn Case, Keh-Jiann Chen, Sanjay Jain, Wolfgang Merkle, James S. Royer. 684-698 [doi]
• On Learning to Coordinate: Random Bits Help, Insightful Normal Forms, and Competency IsomorphismsJohn Case, Sanjay Jain, Franco Montagna, Giulia Simi, Andrea Sorbi. 699-713 [doi]
• Learning All Subfunctions of a FunctionSanjay Jain, Efim B. Kinber, Rolf Wiehagen. 714-728 [doi]
• When Is Small Beautiful?Amiran Ambroladze, John Shawe-Taylor. 729-730 [doi]
• Learning a Function of r Relevant VariablesAvrim Blum. 731-733 [doi]
• Subspace Detection: A Robust Statistics FormulationSanjoy Dasgupta. 734 [doi]
• How Fast Is ::::k::::-Means?Sanjoy Dasgupta. 735 [doi]
• Universal Coding of Zipf DistributionsYoav Freund, Alon Orlitsky, Prasad Santhanam, Junan Zhang. 736-737 [doi]
• An Open Problem Regarding the Convergence of Universal A Priori ProbabilityMarcus Hutter. 738-740 [doi]
• Entropy Bounds for Restricted Convex HullsVladimir Koltchinskii. 741-742 [doi]
• Compressing to VC Dimension Many PointsManfred K. Warmuth. 743-744 [doi]