Learning Theory: 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004, Proceedings(3120 Lecture Notes in Computer Science)

This book constitutes the refereed proceedings of the 17th Annual Conference on Learning Theory, COLT 2004, held in Banff, Canada in July 2004.The 46 revised full papers presented were carefully reviewed and selected from a total of 113 submissions. The papers are organized in topical sections on economics and game theory, online learning, inductive inference, probabilistic models, Boolean function learning, empirical processes, MDL, generalisation, clustering and distributed learning, boosting, kernels and probabilities, kernels and kernel matrices, and open problems.

Table of Contents:
Economics and Game Theory.- Towards a Characterization of Polynomial Preference Elicitation with Value Queries in Combinatorial Auctions.- Graphical Economics.- Deterministic Calibration and Nash Equilibrium.- Reinforcement Learning for Average Reward Zero-Sum Games.- OnLine Learning.- Polynomial Time Prediction Strategy with Almost Optimal Mistake Probability.- Minimizing Regret with Label Efficient Prediction.- Regret Bounds for Hierarchical Classification with Linear-Threshold Functions.- Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary.- Inductive Inference.- Learning Classes of Probabilistic Automata.- On the Learnability of E-pattern Languages over Small Alphabets.- Replacing Limit Learners with Equally Powerful One-Shot Query Learners.- Probabilistic Models.- Concentration Bounds for Unigrams Language Model.- Inferring Mixtures of Markov Chains.- Boolean Function Learning.- PExact = Exact Learning.- Learning a Hidden Graph Using O(log n) Queries Per Edge.- Toward Attribute Efficient Learning of Decision Lists and Parities.- Empirical Processes.- Learning Over Compact Metric Spaces.- A Function Representation for Learning in Banach Spaces.- Local Complexities for Empirical Risk Minimization.- Model Selection by Bootstrap Penalization for Classification.- MDL.- Convergence of Discrete MDL for Sequential Prediction.- On the Convergence of MDL Density Estimation.- Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification.- Generalisation I.- Learning Intersections of Halfspaces with a Margin.- A General Convergence Theorem for the Decomposition Method.- Generalisation II.- Oracle Bounds and Exact Algorithm for Dyadic Classification Trees.- An Improved VC Dimension Bound for Sparse Polynomials.- A New PAC Bound forIntersection-Closed Concept Classes.- Clustering and Distributed Learning.- A Framework for Statistical Clustering with a Constant Time Approximation Algorithms for K-Median Clustering.- Data Dependent Risk Bounds for Hierarchical Mixture of Experts Classifiers.- Consistency in Models for Communication Constrained Distributed Learning.- On the Convergence of Spectral Clustering on Random Samples: The Normalized Case.- Boosting.- Performance Guarantees for Regularized Maximum Entropy Density Estimation.- Learning Monotonic Linear Functions.- Boosting Based on a Smooth Margin.- Kernels and Probabilities.- Bayesian Networks and Inner Product Spaces.- An Inequality for Nearly Log-Concave Distributions with Applications to Learning.- Bayes and Tukey Meet at the Center Point.- Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results.- Kernels and Kernel Matrices.- A Statistical Mechanics Analysis of Gram Matrix Eigenvalue Spectra.- Statistical Properties of Kernel Principal Component Analysis.- Kernelizing Sorting, Permutation, and Alignment for Minimum Volume PCA.- Regularization and Semi-supervised Learning on Large Graphs.- Open Problems.- Perceptron-Like Performance for Intersections of Halfspaces.- The Optimal PAC Algorithm.- The Budgeted Multi-armed Bandit Problem.