Wednesday
8:45am	Welcome and Information
9:00am	Invited Speaker: Christos Papadimitriou (COLT/ICML) (Physics Theatre)
10:00am	Morning Tea (Physics Lawn)
	CSE Seminar Room Ensemble Learners Chair: Patricia Riddle	Physics Theatre Hierarchical Reinforcement Learning Chair: Tom Dietterich	Red Centre (M032) Text Learning Chair: Ian Witten
10:30am	Is Combining Classifiers Better than Selecting the Best One? Saso Dzeroski, Bernard Zenko	Discovering Hierarchy in Reinforcement Learning with HEXQ Bernhard Hengst	Learning word normalization using word suffix and context from unlabeled data Dunja Mladenic
11:00am	A Unified Decomposition of Ensemble Loss for Predicting Ensemble Performance Michael Goebel, Pat Riddle, Mike Barley	Automatic Creation of Useful Macro-Actions in Reinforcement Learning Marc Pickett, Andrew Barto	A New Statistical Approach on Personal Name Extraction Zheng Chen, Feng Zhang
11:30am	Cranking: An Ensemble Method for Combining Rankers using Conditional Probability Models on Permutations Guy Lebanon, John Lafferty	Using Abstract Models of Behaviours to Automatically Generate Reinforcement Learning Hierarchies Malcolm Ryan	IEMS - The Intelligent Email Sorter Elisabeth Crawford, Judy Kay, Eric McCreath
12:00	Active + Semi-supervised Learning = Robust Multi-View Learning Ion Muslea, Steven Minton, Craig Knoblock	Model-based Hierarchical Average-reward Reinforcement Learning Sandeep Seri, Prasad Tadepalli	Combining Labeled and Unlabeled Data for MultiClass Text Categorization Rayid Ghani

	Wednesday
12:30pm	Lunch (Square House)
	CSE Seminar Room Decision Trees Chair: Ross Quinlan	Physics Theatre Reinforcement/Robot Learning Chair: Prasad Tadepalli	Red Centre (M032) Text Learning Chair: Dunja Mladenic
2:00pm	Fast Minimum Training Error Discretization Tapio Elomaa, Juhu Rousu	Hierarchically Optimal Average Reward Reinforcement Learning Mohammad Ghavamzadeh, Sridhar Mahadevan	Partially Supervised Classification of Text Documents Bing Liu, Wee Sun Lee, Philip S. Yu, Xiaoli Li
2:30pm	Learning Decision Trees Using the Area Under the ROC Curve Cesar Ferri, Peter Flach, Jose Hernandez-Orallo	Action Refinement in Reinforcement Learning by Probability Smoothing Thomas Dietterich, Didac Busquets, Ramon Lopez de Mantaras, Carles Sierra	Syllables and other String Kernel Extensions Craig Saunders, Hauke Tschach, John Shawe-Taylor
3:00pm	An Analysis of Functional Trees Joao Gama	Learning Spatial and Temporal Correlation for Navigation in a 2-Dimensional Continuous World Anand Panangadan, Michael Dyer	A Boosted Maximum Entropy Model for Learning Text Chunking Seong-Bae Park, Byoung-Tak Zhang
3:30pm	Afternoon Tea (Physics Lawn)
	CSE Seminar Room Decision Trees Chair: Mike Cameron-Jones	Physics Theatre Reinforcement Learning Chair: Srdihar Mahadevan	Red Centre (M032) Data Mining Chair: Marko Grobelnik
4:00pm	Classification Value Grouping Colin Ho	Scalable Internal-State Policy-Gradient Methods for POMDPs Douglas Aberdeen, Jonathan Baxter	Using Unlabelled Data for Text Classification through Addition of Cluster Parameters Bhavani Raskutti, Adam Kowalczyk, Herman Ferra
4:30pm	Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction Fumio Takechi, Einoshin Suzuki	An epsilon-Optimal Grid-Based Algorithm for Partially Observable Markov Decision Processes Blai Bonet	From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering Dan Klein, Sepandar Kamvar, Christopher Manning
5:00pm	Adaptive View Validation: A First Step Towards Automatic View Detection Ion Muslea, Steven Minton, Craig Knoblock	On the Existence of Fixed Points for Q-Learning and Sarsa in Partially Observable Domains Theodore Perkins, Mark Pendrith	Mining Both Positive and Negative Association Rules Chengqi Zhang, Xindong Wu, Shichao Zhang

	Thursday
9:00am	Invited Speaker: Saso Dzeroski (ICML/ILP) (Physics Theatre)
10:00am	Morning Tea (Physics Lawn)
	Rupert Myers Theatre Support Vector Machines Chair: Alex Smola	Physics Theatre Behavioural Cloning/ Scientific Discovery Chair: Pat Langley	CSE Seminar Room Theory Chair: John Case
10:30am	Anytime Interval-Valued Outputs for Kernel Machines: Fast Support Vector Machine Classification via Distance Geometry Dennis DeCoste	Reinforcement Learning and Shaping: Encouraging Intended Behaviors Adam Laud, Gerald DeJong	Sufficient Dimensionality Reduction - A novel Analysis Principle Amir Globerson, Naftali Tishby
11:00am	Multi-Instance Kernels Thomas Gaertner, Peter Flach, Adam Kowalczyk, Alex Smola, Robert Williamson	Separating Skills from Preference: Using Learning to Program by Reward Daniel Shapiro, Pat Langley	Combining Training Set and Test Set Bounds John Langford
11:30am	Kernels for Semi-Structured Data Hisashi Kashima, Teruo Koyanagi	Learning to Fly by Controlling Dynamic Instabilities David Stirling	Learning k-Reversible Context-Free Grammars from Positive Structural Examples Tim Oates, Devina Desai, Vinay Bhat
12:00	A Fast Dual Algorithm for Kernel Logistic Regression Sathiya Keerthi, Kaibo Duan Shirish Shevade, Aun Poo	Qualitative reverse engineering Dorian Suc, Ivan Bratko	On generalization bounds, projection profile, and margin distribution Ashutosh Garg, Sariel Har-Peled, Dan Roth

	Thursday
12:30pm	Lunch (Square House)
	Rupert Myers Theatre Cost Sensitive Learning Chair: Rob Holte	Physics Theatre Scientific Discovery/ Reinforcement Learning Chair: Ivan Bratko	CSE Seminar Room BayesianMethods Chair: Chenqi Zhang
2:00pm	An Alternate Objective Function for Markovian Fields Sham Kakade, Yee Whye The, Sam Roweis	Inducing Process Models from Continuous Data Pat Langley, Javier Sanchez, Ljupco Todorovski, Saso Dzeroski	Non-Disjoint Discretization for Naive-Bayes Classifiers Ying Yang, Geoffrey I. Webb
2:30pm	Issues in Classifier Evaluation using Optimal Cost Curves Kai Ming Ting	Integrating Experimentation and Guidance in Relational Reinforcement Learning Kurt Driessens, Saso Dzeroski	Numerical Minimum Message Length Inference of Univariate Polynomials Leigh Fitzgibbon, David Dowe, Lloyd Allison
3:00pm	Pruning Improves Heuristic Search for Cost-Sensitive Learning Valentina Bayer Zubek, Thomas Dietterich	Approximately Optimal Approximate Reinforcement Learning Sham Kakade, John Langford	Learning to Share Distributed Probabilistic Beliefs Christopher Leckie, Ramamohanarao Kotagiri
3:30pm	Afternoon Tea (Physics Lawn)
	Rupert Myers Theatre Unsupervised Learning Chair: Eibe Frank	Physics Theatre Reinforcement Learning Chair: Mark Pendrith	CSE Seminar Room BayesianMethods Chair: Geoff Webb
4:00pm	Semi-supervised Clustering by Seeding Sugato Basu, Arindam Banerjee, Raymond Mooney	Competitive Analysis of the Explore/Exploit Tradeoff John Langford, Martin Zinkevich, Sham Kakade	Markov Chain Monte Carlo Sampling using Direct Search Optimization Malcolm Strens, Mark Bernhardt, Nicholas Everett
4:30pm	Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data Joseph Bockhorst, Mark Craven	Investigating the Maximum Likelihood Alternative to TD(lambda) Fletcher Lu, Relu Patrascu, Dale Schuurmans	Exact model averaging with naive Bayesian classifiers Denver Dash, Gregory Cooper
5:00pm	Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based approach Sepandar Kamvar, Dan Klein, Christopher Manning	Coordinated Reinforcement Learning Carlos Guestrin, Michail Lagoudakis, Ronald Parr	MMIHMM: Maximum Mutual Information Hidden Markov Models Nuria Oliver, Ashutosh Garg

	Friday
9:00am	Invited Speaker: Sebastian Thrun (Physics Theatre)
10:00am	Morning Tea (Physics Lawn)
	Rupert Myers Theatre Ensemble Learners Chair: Bernhard Pfharinger	Physics Theatre Feature Selection Chair: Hiroshi Motoda	CSE Seminar Room Inductive Logic Programming Chair: John Lloyd
10:30am	Incorporating Prior Knowledge into Boosting Robert Schapire, Marie Rochery, Mazin Rahim, Narendra Gupta	Refining the Wrapper Approach - Smoothed Error Estimates for Feature Selection Loo-Nin Teow, Hwee Tou Ng Haifeng Liu, Eric Yap	Feature Subset Selection and Inductive Logic Programming Erick Alphonse, Stan Matwin
11:00am	Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation Robert Schapire, Peter Stone, David McAllester, Michael Littman Janos Csirik	Feature Selection with Active Learning Huan Liu, Hiroshi Motoda, Lei Yu	Inductive Logic Programming out of Phase Transition: A bottom-up constraint-based approach Jacques Ales Bianchetti, Celine Rouveirol, Michele Sebag
11:30am	How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness Alexander K. Seewald	Randomized Variable Elimination David Stracuzzi, Paul Utgoff	Graph-Based Relational Concept Learning Jesus Gonzalez Lawrence Holder, Diane Cook
12:00	Towards "Large Margin" Speech Recognizers by Boosting and Discriminative Training Carsten Meyer, Peter Beyerlein	Discriminative Feature Selection via Multiclass Variable Memory Markov Model Noam Slonim, Gill Bejerano, Shai Fine, Naftali Tishby	Descriptive Induction through Subgroup Discovery: A Case Study in a Medical Domain Dragan Gamberger, Nada Lavrac

	Friday
12:30pm	Lunch (Square House)
	Rupert Myers Theatre Support Vector Machines Chair: Peter Flach	Physics Theatre Bayesian Methods Chair: David Dowe	CSE Seminar Room Feature Selection/ Reinforcement Learning Chair: Paul Utgoff
2:00pm	Statistic Behavior and Consistency of Support Vector Machines, Boosting, and Beyond Tong Zhang	Sparse Bayesian Learning for Regression and Classification using Markov Chain Monte Carlo Shien-Shin Tham, Arnaud Doucet, Ramamohanarao Kotagiri,	Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning David Jensen, Jennifer Neville
2:30pm	The Perceptron Algorithm with Uneven Margins Yaoyong Li, Hugo Zaragoza, Ralf Herbrich, John Shawe-Taylor, Jaz Kandola	Modeling for Optimal Probability Prediction Yong Wang, Ian H. Witten	Algorithm-Directed Exploration for Model-Based Reinforcement Learning Carlos Guestrin, Relu Patrascu, Dale Schuurmans
3:00pm	Learning the Kernel Matrix with Semi-Definite Programming Gert Lanckriet, Nello Christianini, Peter Bartlett, Laurent El Ghaoui, Michael Jordan	Representational Upper Bounds of Bayesian Networks Huajie Zhang, Charles Ling	A Necessary Condition of Convergence for Reinforcement Learning with Function Approximation Artur Merke, Ralf Schoknecht
3:30pm	Afternoon Tea (Physics Lawn)
4:00pm	Rupert Myers Theatre Support Vector Machines/ Reinforcement Learning Chair: Alan Blair	Physics Theatre Rule Learning Chair: Ray Mooney	CSE Seminar Room Applications Chair: David Stirling
4:00pm	Diffusion Kernels on Graphs and Other Discrete Structures Risi Kondor, John Lafferty	Learning Decision Rules by Randomized Iterative Local Search Michael Chisholm, Prasad Tadepalli	Stock Trading System Using Reinforcement Learning with Cooperative Agents Jangmin O, Jae Won Lee, Byoung-Tak Zhang
4:30pm	Learning from Scarce Experience Leonid Peshkin, Christian Shelton	Transformation-Based Regression Bjorn Bringmann, Stefan Kramer, Friedrich Neubarth, Hannes Pirker, Gerhard Widmer	Content-Based Image Retrieval Using Multiple-Instance Learning Qi Zhang, Wei Yu, Sally Goldman, Jason Fritts