Accepted papers

Tutorial: Learning Topics in Game-Theoretic Decision Making
Michael Littman - Rutgers University


Invited talk: A General Class of No-Regret Learning Algorithms and Game-Theoretic Equilibria
Amy Greenwald - Brown University
Amir Jafari - Brown University


Preference Elicitation and Query Learning
Avrim Blum - Carnegie Mellon University
Jeffrey Jackson - Duquesne University
Tuomas Sandholm - Carnegie Mellon University
Martin Zinkevich - Carnegie Mellon University


Efficient Algorithms for Online Decision Problems
Adam Kalai - Massachusetts Institute of Technology
Santosh Vempala - Massachusetts Institute of Technology


Positive Definite Rational Kernels
Corinna Cortes - AT\&T Labs
Patrick Haffner - AT\&T Labs
Mehryar Mohri - AT\&T Labs


Bhattacharyya and Expected Likelihood Kernels
Tony Jebara - Columbia University
Risi Kondor - Columbia University


Maximal Margin Classification for Metric Spaces
Matthias Hein - Max Planck Institute for Biological Cybernetics
Olivier Bousquet - Max Planck Institute for Biological Cybernetics


Maximum Margin Algorithms with Boolean Kernels
Roni Khardon - Tufts University
Rocco Servedio - Columbia University


Knowledge-Based Nonlinear Kernel Classifiers
Glenn Fung - The University of Wisconsin-Madison
Olvi Mangasarian - The University of Wisconsin-Madison
Jude Shavlik - The University of Wisconsin-Madison


Fast Kernels for Inexact String Matching
Christina Leslie - Columbia University
Rui Kuang - Columbia University


On Graph Kernels: Hardness Results and Efficient Alternatives
Thomas Gaertner - Fraunhofer AIS, University of Bonn, and University of Bristol
Peter Flach - University of Bristol
Stefan Wrobel - Fraunhofer AIS and University of Bonn


Kernels and Regularization on Graphs
Alexander J. Smola - The Australian National University
Risi Kondor - Columbia University


Data-Dependent Bounds for Multi-Category Classification\\Based on Convex Losses
Ilya Desyatnikov - Technion
Ron Meir - Technion


Comparing Clusterings by the Variation of Information
Marina Meila - University of Washington


Multiplicative Updates for Large Margin Classifiers
Fei Sha - University of Pennsylvania
Lawrence Saul - University of Pennsylvania
Daniel Lee - University of Pennsylvania


Simplified PAC-Bayesian Margin Bounds
David McAllester - Toyota Technological Institute at Chicago


Sparse Kernel Partial Least Squares Regression
Michinari Momma - Rensselaer Polytechnic Institute
Kristin Bennett - Rensselaer Polytechnic Institute


Sparse Probabilistic Regression by Label Partitioning
Shantanu Chakrabartty - The Johns Hopkins University
Gert Cauwenberghs - The Johns Hopkins University
Prof Jayadeva - Indian Institute of Technology


Learning with Rigorous Support Vector Machines
Jinbo Bi - Rensselaer Polytechnic Insitute
Vladimir Vapnik - NEC Laboratories America


Robust Regression by Boosting the Median
Balazs Kegl - University of Montreal


Boosting with Diverse Base Classifiers
Sanjoy Dasgupta - University of California at San Diego
Philip Long - Genome Institute of Singapore


Reducing Kernel Matrix Diagonal Dominance using Semi-Definite Programming
Jaz Kandola - Royal Holloway University of London
Thore Graepel - Microsoft Research
John Shawe-Taylor - Royal Holloway University of London


Optimal Rates of Aggregation
Alexandre Tsybakov - Universit\'{e} Paris 6


Distance-Based Classification with Lipschitz Functions
Ulrike von Luxburg - Max Planck Institute for Biological Cybernetics
Olivier Bousquet - Max Planck Institute for Biological Cybernetics


Random Subclass Bounds
Shahar Mendelson - The Australian National University
Petra Philips - The Australian National University


PAC-MDL Bounds
Avrim Blum - Carnegie Mellon University
John Langford - IBM TJ Watson Research Center


Universal Well-Calibrated Algorithm for On-line Classification
Vladimir Vovk - Royal Holloway University of London


Learning Probabilistic Linear-Threshold Classifiers via\\Selective Sampling
Nicol\`{o} Cesa-Bianchi - Universit\`{a} degli Studi di Milano
Alex Conconi - Universit\`{a} degli Studi di Milano
Claudio Gentile - Universit\`{a} dell'Insubria


Learning Algorithms for Enclosing Points in Bregmanian Spheres
Koby Crammer - The Hebrew University of Jerusalem
Yoram Singer - The Hebrew University of Jerusalem


Internal Regret in On-line Portfolio Selection
Gilles Stoltz - Universit\'{e} Paris-Sud
Gabor Lugosi - University Pompeu Fabra
Download the slides from the presentation.


Lower Bounds on the Sample Complexity of Exploration in the\\Multi-Armed Bandit Problem
Shie Mannor - Massachusetts Institute of Technology
John Tsitsiklis - Massachusetts Institute of Technology


Smooth epsilon-Insensitive Regression by Loss Symmetrization
Ofer Dekel - The Hebrew University of Jerusalem
Shai Shalev-Shwartz - The Hebrew University of Jerusalem
Yoram Singer - The Hebrew University of Jerusalem


On Finding Large Conjunctive Clusters
Nina Mishra - HP Labs/Stanford University
Dana Ron - Tel-Aviv University
Ram Swaminathan - HP Labs


Learning Arithmetic Circuits via Partial Derivatives
Adam Klivans - Harvard University
Amir Shpilka - Harvard University and Massachusetts Institute of Technology


Using a Linear Fit to Determine Monotonicity Directions
Malik Magdon-Ismail - RPI
Joseph Sill - Plumtree Software


Generalization Bounds for Voting Classifiers Based on\\Sparsity and Clustering
Vladimir Koltchinskii - The University of New Mexico
Dmitry Panchenko - Massachusetts Institute of Technology
Savina Andonova - Boston University


Sequence Prediction Based on Monotone Complexity
Marcus Hutter - IDSIA


How Many Strings Are Easy to Predict?
Yuri Kalnishkan - Royal Holloway University of London
Vladimir Vovk - Royal Holloway University of London
Michael V. Vyugin - Royal Holloway University of London


Polynomial Certificates for Propositional Classes
Marta Arias - Tufts University
Roni Khardon - Tufts University
Rocco Servedio - Columbia University


On-line Learning with Imperfect Monitoring
Shie Mannor - Massachusetts Institute of Technology
Nahum Shimkin - Technion


Exploiting Task Relatedness for Multiple Task Learning
Shai Ben-David - Cornell University and Technion
Reba Schuller - Cornell University


Approximate Equivalence of Markov Decision Processes
Eyal Even-Dar - Tel Aviv University
Yishay Mansour - Tel Aviv University


An Information Theoretic Tradeoff between Complexity and Accuracy
Ran Gilad-Bachrach - The Hebrew University of Jerusalem
Amir Navot - The Hebrew University of Jerusalem
Naftali Tishby - The Hebrew University of Jerusalem


Learning Random log-depth Decision Trees under the Uniform\\Distribution
Jeffrey Jackson - Duquesne University
Rocco Servedio - Columbia University


Projective DNF Formulae and Their Revision
Robert Sloan - University of Illinois at Chicago
Balazs Szorenyi - University of Szeged
Gyorgy Turan - University of Illinois at Chicago


Learning with Equivalence Constraints, and the Relation to\\Multiclass Learning
Aharon Bar Hillel - The Hebrew University of Jerusalem
Daphna Weinshall - The Hebrew University of Jerusalem


Tutorial: Machine Learning Methods in Natural Language Processing
Michael Collins - Massachusetts Institute of Technology


Invited talk: Learning from Uncertain Data
Mehryar Mohri - AT\&T Labs


Invited talk: Learning and Parsing Stochastic Unification-Based Grammars
Mark Johnson - Brown University


Generality's Price: Inescapable Deficiencies in Machine-Learned\\Programs
John Case - University of Delaware
Keh-Jiann Chen - Academia Sinica
Sanjay Jain - National University of Singapore
Wolfgang Merkle - Universit\"{a}t Heidelberg
James Royer - Syracuse University
Download slides from the presentation.


On Learning To Coordinate: Random Bits Help, Insightful Normal\\Forms, and Competency Isomorphisms
John Case - University of Delaware
Sanjay Jain - National University of Singapore
Franco Montagna - University of Siena
Giulia Simi - University of Siena
Andrea Sorbi - University of Siena
Download slides from the presentation.


Learning All Subfunctions of a Function
Sanjay Jain - National University of Singapore
Efim Kinber - Sacred Heart University
Rolf Wiehagen - University of Kaiserslautern

Open Problems


When Is Small Beautiful?
Amiran Ambroladze - Lund University
John Shawe-Taylor - Royal Holloway University of London


Learning a Function of r Relevant Variables
Avrim Blum - Carnegie Mellon University


Subspace Detection: A Robust Statistics Formulation
Sanjoy Dasgupta - University of California at San Diego


How Fast Is k-Means?
Sanjoy Dasgupta - University of California at San Diego


Universal Coding of Zipf Distributions
Yoav Freund - Mitsubishi Electric Research Labs
Alon Orlitsky - University of California at San Diego
Prasad Santhanam - University of California at San Diego
Junan Zhang - University of California at San Diego


An Open Problem Regarding the Convergence of Universal A\\Priori Probability
Marcus Hutter - IDSIA


Entropy Bounds for Restricted Convex Hulls
Vladimir Koltchinskii - University of New Mexico


Compressing to VC Dimension Many Points
Manfred K. Warmuth - University of California at Santa Cruz


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