17. COLT 2004: Banff, Canada

Economics and Game Theory

• Paolo Santi, Vincent Conitzer, Tuomas Sandholm:
  Towards a Characterization of Polynomial Preference Elicitation with Value Queries in Combinatorial Auctions (Extended Abstract). 1-16
  
• Sham Kakade, Michael J. Kearns, Luis E. Ortiz:
  Graphical Economics. 17-32
  
• Sham Kakade, Dean P. Foster:
  Deterministic Calibration and Nash Equilibrium. 33-48
  
• Shie Mannor:
  Reinforcement Learning for Average Reward Zero-Sum Games. 49-63
  

OnLine Learning

• Nader H. Bshouty:
  Polynomial Time Prediction Strategy with Almost Optimal Mistake Probability. 64-76
  
• Nicolò Cesa-Bianchi, Gábor Lugosi, Gilles Stoltz:
  Minimizing Regret with Label Efficient Prediction. 77-92
  
• Nicolò Cesa-Bianchi, Alex Conconi, Claudio Gentile:
  Regret Bounds for Hierarchical Classification with Linear-Threshold Functions. 93-108
  
• H. Brendan McMahan, Avrim Blum:
  Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary. 109-123
  

Inductive Inference

• François Denis, Yann Esposito:
  Learning Classes of Probabilistic Automata. 124-139
  
• Daniel Reidenbach:
  On the Learnability of E-pattern Languages over Small Alphabets. 140-154
  
• Steffen Lange, Sandra Zilles:
  Replacing Limit Learners with Equally Powerful One-Shot Query Learners. 155-169
  

Probabilistic Models

• Evgeny Drukh, Yishay Mansour:
  Concentration Bounds for Unigrams Language Model. 170-185
  
• Tugkan Batu, Sudipto Guha, Sampath Kannan:
  Inferring Mixtures of Markov Chains. 186-199
  

Boolean Function Learning

• Dmitry Gavinsky, Avi Owshanko:
  PExact = Exact Learning. 200-209
  
• Dana Angluin, Jiang Chen:
  Learning a Hidden Graph Using O(log n) Queries Per Edge. 210-223
  
• Adam R. Klivans, Rocco A. Servedio:
  Toward Attribute Efficient Learning of Decision Lists and Parities. 224-238
  

Empirical Processes

• Ha Quang Minh, Thomas Hofmann:
  Learning Over Compact Metric Spaces. 239-254
  
• Charles A. Micchelli, Massimiliano Pontil:
  A Function Representation for Learning in Banach Spaces. 255-269
  
• Peter L. Bartlett, Shahar Mendelson, Petra Philips:
  Local Complexities for Empirical Risk Minimization. 270-284
  
• Magalie Fromont:
  Model Selection by Bootstrap Penalization for Classification. 285-299
  

MDL

• Jan Poland, Marcus Hutter:
  Convergence of Discrete MDL for Sequential Prediction. 300-314
  
• Tong Zhang:
  On the Convergence of MDL Density Estimation. 315-330
  
• Peter Grünwald, John Langford:
  Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification. 331-347
  

Generalisation I

• Adam R. Klivans, Rocco A. Servedio:
  Learning Intersections of Halfspaces with a Margin. 348-362
  
• Nikolas List, Hans-Ulrich Simon:
  A General Convergence Theorem for the Decomposition Method. 363-377
  

Generalisation II

• Gilles Blanchard, Christin Schäfer, Yves Rozenholc:
  Oracle Bounds and Exact Algorithm for Dyadic Classification Trees. 378-392
  
• Michael Schmitt:
  An Improved VC Dimension Bound for Sparse Polynomials. 393-407
  
• Peter Auer, Ronald Ortner:
  A New PAC Bound for Intersection-Closed Concept Classes. 408-414
  

Clustering and Distributed Learning

• Shai Ben-David:
  A Framework for Statistical Clustering with a Constant Time Approximation Algorithms for K-Median Clustering. 415-426
  
• Arik Azran, Ron Meir:
  Data Dependent Risk Bounds for Hierarchical Mixture of Experts Classifiers. 427-441
  
• Joel B. Predd, Sanjeev R. Kulkarni, Harold Vincent Poor:
  Consistency in Models for Communication Constrained Distributed Learning. 442-456
  
• Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin:
  On the Convergence of Spectral Clustering on Random Samples: The Normalized Case. 457-471
  

Boosting

• Miroslav Dudík, Steven J. Phillips, Robert E. Schapire:
  Performance Guarantees for Regularized Maximum Entropy Density Estimation. 472-486
  
• Adam Kalai:
  Learning Monotonic Linear Functions. 487-501
  
• Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies:
  Boosting Based on a Smooth Margin. 502-517
  

Kernels and Probabilities

• Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans-Ulrich Simon:
  Bayesian Networks and Inner Product Spaces. 518-533
  
• Constantine Caramanis, Shie Mannor:
  An Inequality for Nearly Log-Concave Distributions with Applications to Learning. 534-548
  
• Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
  Bayes and Tukey Meet at the Center Point. 549-563
  
• Peter L. Bartlett, Ambuj Tewari:
  Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results. 564-578
  

Kernels and Kernel Matrices

• David C. Hoyle, Magnus Rattray:
  A Statistical Mechanics Analysis of Gram Matrix Eigenvalue Spectra. 579-593
  
• Laurent Zwald, Olivier Bousquet, Gilles Blanchard:
  Statistical Properties of Kernel Principal Component Analysis. 594-608
  
• Tony Jebara:
  Kernelizing Sorting, Permutation, and Alignment for Minimum Volume PCA. 609-623
  
• Mikhail Belkin, Irina Matveeva, Partha Niyogi:
  Regularization and Semi-supervised Learning on Large Graphs. 624-638
  

Open Problems

• Adam R. Klivans, Rocco A. Servedio:
  Perceptron-Like Performance for Intersections of Halfspaces. 639-640
  
• Manfred K. Warmuth:
  The Optimal PAC Algorithm. 641-642
  
• Omid Madani, Daniel J. Lizotte, Russell Greiner:
  The Budgeted Multi-armed Bandit Problem. 643-645
  

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