Learning Theory

This volume contains papers presented at the 17th Annual Conference on Le- ning Theory (previously known as the Conference on Computational Learning Theory) held in Ban?, Canada from July 1 to 4, 2004. The technical program contained 43 papers selected from 107 submissions, 3 open problems selected from among 6 contributed, and 3 invited lectures. The invited lectures were given by Michael Kearns on Game Theory, Automated Trading and Social Networks , Moses Charikar on Algorithmic Aspects of - nite Metric Spaces , and Stephen Boyd on Convex Optimization, Semide?nite Programming, and Recent Applications . These papers were not included in this volume. The Mark Fulk Award is presented annually for the best paper co-authored by a student. Thisyear theMark Fulk award wassupplemented with two further awards funded by the Machine Learning Journal and the National Information Communication Technology Centre, Australia (NICTA). We were therefore able toselectthreestudentpapersforprizes.ThestudentsselectedwereMagalieF- montforthesingle-authorpaper ModelSelectionbyBootstrapPenalizationfor Classi?cation , Daniel Reidenbach for the single-author paper On the Lear- bility of E-Pattern Languages over Small Alphabets , and Ran Gilad-Bachrach for the paper Bayes and Tukey Meet at the Center Point (co-authored with Amir Navot and Naftali Tishby).

Includes supplementary material: sn.pub/extras

Inhalt
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.