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ORCID17th COLT 2004: Banff, Canada
- John Shawe-Taylor, Yoram Singer:
Learning Theory, 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004, Proceedings. Lecture Notes in Computer Science 3120, Springer 2004, ISBN 3-540-22282-0
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 M. Kakade, Michael J. Kearns, Luis E. Ortiz
:
Graphical Economics. 17-32 - Sham M. 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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