Learning Theory

17th Annual Conference on Learning Theory, Colt 2004, Banff, Canada, July 1-4, 2004, Proceedings

By: John Shawe-Taylor (Editor), Yoram Singer (Editor)

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Paperback | 17 June 2004

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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).

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Non-Fiction Reference, Information & Interdisciplinary Subjects Research & Information Information theory Cybernetics & Systems Theory Mathematics Mathematical Foundation Mathematical Logic Computing & I.T.Computer Science Artificial Intelligence Machine Learning

Computer Programming & Software Development Algorithms & Data Structures

Economics and Game Theory
Towards a Characterization of Polynomial Preference Elicitation with Value Queries in Combinatorial Auctions	p. 1
Graphical Economics	p. 17
Deterministic Calibration and Nash Equilibrium	p. 33
Reinforcement Learning for Average Reward Zero-Sum Games	p. 49
OnLine Learning
Polynomial Time Prediction Strategy with Almost Optimal Mistake Probability	p. 64
Minimizing Regret with Label Efficient Prediction	p. 77
Regret Bounds for Hierarchical Classification with Linear-Threshold Functions	p. 93
Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary	p. 109
Inductive Inference
Learning Classes of Probabilistic Automata	p. 124
On the Learnability of E-pattern Languages over Small Alphabets	p. 140
Replacing Limit Learners with Equally Powerful One-Shot Query Learners	p. 155
Probabilistic Models
Concentration Bounds for Unigrams Language Model	p. 170
Inferring Mixtures of Markov Chains	p. 186
Boolean Function Learning
PExact = Exact Learning	p. 200
Learning a Hidden Graph Using O(log n) Queries Per Edge	p. 210
Toward Attribute Efficient Learning of Decision Lists and Parities	p. 224
Empirical Processes
Learning Over Compact Metric Spaces	p. 239
A Function Representation for Learning in Banach Spaces	p. 255
Local Complexities for Empirical Risk Minimization	p. 270
Model Selection by Bootstrap Penalization for Classification	p. 285
MDL
Convergence of Discrete MDL for Sequential Prediction	p. 300
On the Convergence of MDL Density Estimation	p. 315
Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification	p. 331
Generalisation I
Learning Intersections of Halfspaces with a Margin	p. 348
A General Convergence Theorem for the Decomposition Method	p. 363
Generalisation II
Oracle Bounds and Exact Algorithm for Dyadic Classification Trees	p. 378
An Improved VC Dimension Bound for Sparse Polynomials	p. 393
A New PAC Bound for Intersection-Closed Concept Classes	p. 408
Clustering and Distributed Learning
A Framework for Statistical Clustering with a Constant Time Approximation Algorithms for i-Median Clustering	p. 415
Data Dependent Risk Bounds for Hierarchical Mixture of Experts Classifiers	p. 427
Consistency in Models for Communication Constrained Distributed Learning	p. 442
On the Convergence of Spectral Clustering on Random Samples: The Normalized Case	p. 457
Boosting
Performance Guarantees for Regularized Maximum Entropy Density Estimation	p. 472
Learning Monotonic Linear Functions	p. 487
Boosting Based on a Smooth Margin	p. 502
Kernels and Probabilities
Bayesian Networks and Inner Product Spaces	p. 518
An Inequality for Nearly Log-Concave Distributions with Applications to Learning	p. 534
Bayes and Tukey Meet at the Center Point	p. 549
Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results	p. 564
Kernels and Kernel Matrices
A Statistical Mechanics Analysis of Gram Matrix Eigenvalue Spectra	p. 579
Statistical Properties of Kernel Principal Component Analysis	p. 594
Kernelizing Sorting, Permutation, and Alignment for Minimum Volume PCA	p. 609
Regularization and Semi-supervised Learning on Large Graphs	p. 624
Open Problems
Perceptron-Like Performance for Intersections of Halfspaces	p. 639
The Optimal PAC Algorithm	p. 641
The Budgeted Multi-armed Bandit Problem	p. 643
Author Index	p. 647
Table of Contents provided by Publisher. All Rights Reserved.

Learning Theory

17th Annual Conference on Learning Theory, Colt 2004, Banff, Canada, July 1-4, 2004, Proceedings

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