Front matter
1
Tutorial: Learning Topics in Game-Theoretic Decision Making
Michael L. Littman
2-12
A General Class of No-Regret Learning Algorithms and Game-Theoretic Equilibria
Amy Greenwald and Amir Jafari
13-25
Preference Elicitation and Query Learning
Avrim Blum, Jeffrey C. Jackson, Tuomas Sandholm and Martin Zinkevich
26-40
Efficient Algorithms for Online Decision Problems
Adam Kalai and Santosh Vempala
41-56
Positive Definite Rational Kernels
Corinna Cortes, Patrick Haffner and Mehryar Mohri
57-71
Bhattacharyya and Expected Likelihood Kernels
Tony Jebara and Risi Kondor
72-86
Maximal Margin Classification for Metric Spaces
Matthias Hein and Olivier Bousquet
87-101
Maximum Margin Algorithms with Boolean Kernels
Roni Khardon and Rocco A. Servedio
102-113
Knowledge-Based Nonlinear Kernel Classifiers
Glenn M. Fung, Olvi L. Mangasarian and Jude W. Shavlik
114-128
Fast Kernels for Inexact String Matching
Christina Leslie and Rui Kuang
129-143
On Graph Kernels: Hardness Results and Efficient Alternatives
Thomas Gärtner, Peter Flach and Stefan Wrobel
144-158
Kernels and Regularization on Graphs
Alexander J. Smola and Risi Kondor
159-172
Data-Dependent Bounds for Multi-category Classification Based on Convex Losses
Ilya Desyatnikov and Ron Meir
173-187
Comparing Clusterings by the Variation of Information
Marina Meilă
188-202
Multiplicative Updates for Large Margin Classifiers
Fei Sha, Lawrence K. Saul and Daniel D. Lee
203-215
Simplified PAC-Bayesian Margin Bounds
David McAllester
216-230
Sparse Kernel Partial Least Squares Regression
Michinari Momma and Kristin P. Bennett
231-242
Sparse Probability Regression by Label Partitioning
Shantanu Chakrabartty, Gert Cauwenberghs and Jayadeva
243-257
Learning with Rigorous Support Vector Machines
Jinbo Bi and Vladimir N. Vapnik
258-272
Robust Regression by Boosting the Median
Balázs Kégl
273-287
Boosting with Diverse Base Classifiers
Sanjoy Dasgupta and Philip M. Long
288-302
Reducing Kernel Matrix Diagonal Dominance Using Semi-definite Programming
Jaz Kandola, Thore Graepel and John Shawe-Taylor
303-313
Optimal Rates of Aggregation
Alexandre B. Tsybakov
314-328
Distance-Based Classification with Lipschitz Functions
Ulrike von Luxburg and Olivier Bousquet
329-343
Random Subclass Bounds
Shahar Mendelson and Petra Philips
344-357
PAC-MDL Bounds
Avrim Blum and John Langford
358-372
Universal Well-Calibrated Algorithm for On-Line Classification
Vladimir Vovk
373-387
Learning Probabilistic Linear-Threshold Classifiers via Selective Sampling
Nicolò Cesa-Bianchi, Alex Conconi and Claudio Gentile
388-402
Learning Algorithms for Enclosing Points in Bregmanian Spheres
Koby Crammer and Yoram Singer
403-417
Internal Regret in On-Line Portfolio Selection
Gilles Stoltz and Gábor Lugosi
418-432
Lower Bounds on the Sample Complexity of Exploration in the Multi-armed Bandit Problem
Shie Mannor and John N. Tsitsiklis
433-447
Smooth ε-Insensitive Regression by Loss Symmetrization
Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer
448-462
On Finding Large Conjunctive Clusters
Nina Mishra, Dana Ron and Ram Swaminathan
463-476
Learning Arithmetic Circuits via Partial Derivatives
Adam R. Klivans and Amir Shpilka
477-491
Using a Linear Fit to Determine Monotonicity Directions
Malik Magdon-Ismail and Joseph Sill
492-505
Generalization Bounds for Voting Classifiers Based on Sparsity and Clustering
Vladimir Koltchinskii, Dmitry Panchenko and Savina Andonova
506-521
Sequence Prediction Based on Monotone Complexity
Marcus Hutter
522-536
How Many Strings Are Easy to Predict?
Yuri Kalnishkan, Volodya Vovk and Michael V. Vyugin
537-551
Polynomial Certificates for Propositional Classes
Marta Arias, Roni Khardon and Rocco A. Servedio
552-566
On-Line Learning with Imperfect Monitoring
Shie Mannor and Nahum Shimkin
567-580
Exploiting Task Relatedness for Multiple Task Learning
Shai Ben-David and Reba Schuller
581-594
Approximate Equivalence of Markov Decision Processes
Eyal Even-Dar and Yishay Mansour
595-609
An Information Theoretic Tradeoff between Complexity and Accuracy
Ran Gilad-Bachrach, Amir Navot and Naftali Tishby
610-624
Learning Random Log-Depth Decision Trees under the Uniform Distribution
Jeffrey C. Jackson and Rocco A. Servedio
625-639
Projective DNF Formulae and Their Revision
Robert H. Sloan, Balázs Szörényi and György Turán
640-654
Learning with Equivalence Constraints and the Relation to Multiclass Learning
Aharon Bar-Hillel and Daphna Weinshall
655
Tutorial: Machine Learning Methods in Natural Language Processing
Michael Collins
656-670
Learning from Uncertain Data
Mehryar Mohri
671-683
Learning and Parsing Stochastic Unification-Based Grammars
Mark Johnson
684-698
Generality’s Price
Inescapable Deficiencies in Machine-Learned Programs
John Case, Keh-Jiann Chen, Sanjay Jain, Wolfgang Merkle and James S. Royer
699-713
On Learning to Coordinate
Random Bits Help, Insightful Normal Forms, and Competency Isomorphisms
John Case, Sanjay Jain, Franco Montagna, Giulia Simi and Andrea Sorbi
714-728
Learning All Subfunctions of a Function
Sanjay Jain, Efim Kinber and Rolf Wiehagen
729-730
When Is Small Beautiful?
Amiran Ambroladze and John Shawe-Taylor
731-733
Learning a Function of r Relevant Variables
Avrim Blum
734
Subspace Detection: A Robust Statistics Formulation
Sanjoy Dasgupta
735
How Fast Is k-Means?
Sanjoy Dasgupta
736-737
Universal Coding of Zipf Distributions
Yoav Freund, Alon Orlitsky, Prasad Santhanam and Junan Zhang
738-740
An Open Problem Regarding the Convergence of Universal A Priori Probability
Marcus Hutter
741-742
Entropy Bounds for Restricted Convex Hulls
Vladimir Koltchinskii
743-744
Compressing to VC Dimension Many Points
Manfred K. Warmuth
Back matter