Wednesday			Thursday			Friday
Christos Papadimitriou			Saso Dzeroski			Sebastian Thrun
Morning Tea			Morning Tea			Morning Tea
ENSEMBLE Is Combining Classifiers Better than Selecting the Best One? Saso Dzeroski Bernard Zenko	HRL Discovering Hierarchy in Reinforcement Learning with HEXQ Bernhard Hengst	TEXT Learning word normalization using word suffix and context from unlabeled data Dunja Mladenic	BC/DISC Reinforcement Learning and Shaping: Encouraging Intended Behaviors Adam Laud Gerald DeJong	SVM Anytime Interval-Valued Outputs for Kernel Machines: Fast Support Vector Machine Classification via Distance Geometry Dennis DeCoste	COLT Sufficient Dimensionality Reduction - A novel Analysis Principle Amir Globerson Naftali Tishby	ENSEMBLE Incorporating Prior Knowledge into Boosting Robert Schapire Marie Rochery Mazin Rahim Narendra Gupta	FEATURE Refining the Wrapper Approach - Smoothed Error Estimates for Feature Selection Loo-Nin Teow Hwee Tou Ng Haifeng Liu Eric Yap	ILP Feature Subset Selection and Inductive Logic Programming Erick Alphonse Stan Matwin
ENSEMBLE A Unified Decomposition of Ensemble Loss for Predicting Ensemble Performance Michael Goebel Pat Riddle Mike Barley	HRL Automatic Creation of Useful Macro-Actions in Reinforcement Learning Marc Pickett Andrew Barto	TEXT A New Statistical Approach on Personal Name Extraction Zheng Chen Feng Zhang	BC/DISC Separating Skills from Preference: Using Learning to Program by Reward Daniel Shapiro Pat Langley	SVM Multi-Instance Kernels Thomas Gaertner Peter Flach Adam Kowalczyk Alex Smola Robert Williamson	COLT Combining Training Set and Test Set Bounds John Langford	ENSEMBLE Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation Robert Schapire Peter Stone David McAllester Michael Littman Janos Csirik	FEATURE Feature Selection with Active Learning Huan Liu Hiroshi Motoda Lei Yu	ILP Inductive Logic Programming out of Phase Transition: A bottom-up constraint-based approach Jacques Ales Bianchetti Celine Rouveirol Michele Sebag
ENSEMBLE Cranking: An Ensemble Method for Combining Rankers using Conditional Probability Models on Permutations Guy Lebanon John Lafferty	HRL Using Abstract Models of Behaviours to Automatically Generate Reinforcement Learning Hierarchies Malcolm Ryan	TEXT IEMS - The Intelligent Email Sorter Elisabeth Crawford Judy Kay Eric McCreath	BC/DISC Learning to Fly by Controlling Dynamic Instabilities David Stirling	SVM Kernels for Semi-Structured Data Hisashi Kashima Teruo Koyanagi	COLT Learning k-Reversible Context-Free Grammars from Positive Structural Examples Tim Oates Devina Desai Vinay Bhat	ENSEMBLE How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness Alexander K. Seewald	FEATURE Randomized Variable Elimination David Stracuzzi Paul Utgoff	ILP Graph-Based Relational Concept Learning Jesus Gonzalez Lawrence Holder Diane Cook
ENSEMBLE Active + Semi-supervised Learning = Robust Multi-View Learning Ion Muslea Steven Minton Craig Knoblock	HRL Model-based Hierarchical Average-reward Reinforcement Learning Sandeep Seri Prasad Tadepalli	TEXT Combining Labeled and Unlabeled Data for MultiClass Text Categorization Rayid Ghani	BC/DISC Qualitative reverse engineering Dorian Suc Ivan Bratko	SVM A Fast Dual Algorithm for Kernel Logistic Regression Sathiya Keerthi Kaibo Duan Shirish Shevade Aun Poo	COLT On generalization bounds, projection profile, and margin distribution Ashutosh Garg Sariel Har-Peled Dan Roth	ENSEMBLE Towards "Large Margin" Speech Recognizers by Boosting and Discriminative Training Carsten Meyer Peter Beyerlein	FEATURE Discriminative Feature Selection via Multiclass Variable Memory Markov Model Noam Slonim Gill Bejerano Shai Fine Naftali Tishby	RULE Descriptive Induction through Subgroup Discovery: A Case Study in a Medical Domain Dragan Gamberger Nada Lavrac
Lunch			Lunch			Lunch
TREES Fast Minimum Training Error Discretization Tapio Elomaa Juhu Rousu	HRL Hierarchically Optimal Average Reward Reinforcement Learning Mohammad Ghavamzadeh Sridhar Mahadevan	TEXT Partially Supervised Classification of Text Documents Bing Liu Wee Sun Lee Philip S. Yu Xiaoli Li	BC/DISC Inducing Process Models from Continuous Data Pat Langley Javier Sanchez Ljupco Todorovski Saso Dzeroski	COST An Alternate Objective Function for Markovian Fields Sham Kakade Yee Whye Teh Sam Roweis	BAYES Non-Disjoint Discretization for Naive-Bayes Classifiers Ying Yang Geoffrey I. Webb	SVM Statistic Behavior and Consistency of Support Vector Machines, Boosting, and Beyond Tong Zhang	BAYES Sparse Bayesian Learning for Regression and Classification using Markov Chain Monte Carlo Shien-Shin Tham Arnaud Doucet Ramamohanarao Kotagiri	FEATURE Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning David Jensen Jennifer Neville
TREES Learning Decision Trees Using the Area Under the ROC Curve Cesar Ferri Peter Flach Jose Hernandez-Orallo	RL Action Refinement in Reinforcement Learning by Probability Smoothing Thomas Dietterich Didac Busquets Ramon Lopez de Mantaras Carles Sierra	TEXT Syllables and other String Kernel Extensions Craig Saunders Hauke Tschach John Shawe-Taylor	RL Integrating Experimentation and Guidance in Relational Reinforcement Learning Kurt Driessens Saso Dzeroski	COST Issues in Classifier Evaluation using Optimal Cost Curves Kai Ming Ting	BAYES Numerical Minimum Message Length Inference of Univariate Polynomials Leigh Fitzgibbon David Dowe Lloyd Allison	SVM The Perceptron Algorithm with Uneven Margins Yaoyong Li Hugo Zaragoza Ralf Herbrich John Shawe-Taylor Jaz Kandola	BAYES Modeling for Optimal Probability Prediction Yong Wang Ian H. Witten	RL Algorithm-Directed Exploration for Model-Based Reinforcement Learning Carlos Guestrin Relu Patrascu Dale Schuurmans
TREES An Analysis of Functional Trees Joao Gama	BC/DISC Learning Spatial and Temporal Correlation for Navigation in a 2-Dimensional Continuous World Anand Panangadan Michael Dyer	TEXT A Boosted Maximum Entropy Model for Learning Text Chunking Seong-Bae Park Byoung-Tak Zhang	RL Approximately Optimal Approximate Reinforcement Learning Sham Kakade John Langford	COST Pruning Improves Heuristic Search for Cost-Sensitive Learning Valentina Bayer Zubek Thomas Dietterich	BAYES Learning to Share Distributed Probabilistic Beliefs Christopher Leckie Ramamohanarao Kotagiri	SVM Learning the Kernel Matrix with Semi-Definite Programming Gert Lanckriet Nello Christianini Peter Bartlett Laurent El Ghaoui Michael Jordan	BAYES Representational Upper Bounds of Bayesian Networks Huajie Zhang Charles Ling	RL A Necessary Condition of Convergence for Reinforcement Learning with Function Approximation Artur Merke Ralf Schoknecht
Afternoon Tea			Afternoon Tea			Afternoon Tea
TREES Classification Value Grouping Colin Ho	RL Scalable Internal-State Policy-Gradient Methods for POMDPs Douglas Aberdeen Jonathan Baxter	TEXT Using Unlabelled Data for Text Classification through Addition of Cluster Parameters Bhavani Raskutti Adam Kowalczyk Herman Ferra	RL Competitive Analysis of the Explore/Exploit Tradeoff John Langford Martin Zinkevich Sham Kakade	UNSUP Semi-supervised Clustering by Seeding Sugato Basu Arindam Banerjee Raymond Mooney	BAYES Markov Chain Monte Carlo Sampling using Direct Search Optimization Malcolm Strens Mark Bernhardt Nicholas Everett	SVM Diffusion Kernels on Graphs and Other Discrete Structures Risi Kondor John Lafferty	RULE Learning Decision Rules by Randomized Iterative Local Search Michael Chisholm Prasad Tadepalli	RL Stock Trading System Using Reinforcement Learning with Cooperative Agents Jangmin O Jae Won Lee Byoung-Tak Zhang
TREES Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction Fumio Takechi Einoshin Suzuki	RL An epsilon-Optimal Grid-Based Algorithm for Partially Observable Markov Decision Processes Blai Bonet	UNSUP From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering Dan Klein Sepandar Kamvar Christopher Manning	RL Investigating the Maximum Likelihood Alternative to TD(lambda) Fletcher Lu Relu Patrascu Dale Schuurmans	UNSUP Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data Joseph Bockhorst Mark Craven	BAYES Exact model averaging with naive Bayesian classifiers Denver Dash Gregory Cooper	RL Learning from Scarce Experience Leonid Peshkin Christian Shelton	RULE Transformation-Based Regression Bjorn Bringmann Stefan Kramer Friedrich Neubarth Hannes Pirker Gerhard Widmer	MULT Content-Based Image Retrieval Using Multiple-Instance Learning Qi Zhang Wei Yu Sally Goldman Jason Fritts
TREES Adaptive View Validation: A First Step Towards Automatic View Detection Ion Muslea Steven Minton Craig Knoblock	RL On the Existence of Fixed Points for Q-Learning and Sarsa in Partially Observable Domains Theodore Perkins Mark Pendrith	RULE Mining Both Positive and Negative Association Rules Xindong Wu Shichao Zhang	RL Coordinated Reinforcement Learning Carlos Guestrin Michail Lagoudakis Ronald Parr	UNSUP Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based approach Sepandar Kamvar Dan Klein Christopher Manning	BAYES MMIHMM: Maximum Mutual Information Hidden Markov Models Nuria Oliver Ashutosh Garg