
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 MacroActions 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 +
Semisupervised Learning = Robust MultiView Learning Ion Muslea, Steven Minton,
Craig Knoblock

Modelbased
Hierarchical Averagereward 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, 
2:30pm 
Learning Decision
Trees Using the Area Under the ROC Curve Cesar Ferri,
Peter Flach, 
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, 
3:00pm 
An Analysis of
Functional Trees Joao Gama

Learning Spatial
and Temporal Correlation for Navigation in a 2Dimensional Continuous World Anand Panangadan, Michael Dyer

A Boosted
Maximum Entropy Model for Learning Text Chunking SeongBae Park, ByoungTak
Zhang

3:30pm 
Afternoon Tea (Physics Lawn) 


CSE Seminar Room
Decision Trees Chair: Mike CameronJones 
Physics Theatre
Reinforcement Learning Chair: Srdihar Mahadevan 
Red Centre (M032)
Data Mining Chair: Marko Grobelnik 
4:00pm 
Classification
Value Grouping Colin Ho

Scalable
InternalState PolicyGradient 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 GainRatio SubsetSplit Test for a SetValued Attribute in Decision
Tree Induction Fumio Takechi, Einoshin Suzuki

An
epsilonOptimal GridBased Algorithm for Partially Observable Markov Decision
Processes Blai Bonet

From
Instancelevel Constraints to SpaceLevel Constraints: Making the Most of
Prior Knowledge in Data Clustering Dan Klein,
Sepandar Kamvar, 
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 QLearning and Sarsa in Partially Observable Domains Theodore Perkins, Mark Pendrith

Mining Both
Positive and Negative Association Rules Chengqi Zhang,
Xindong Wu, 

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
IntervalValued 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 
MultiInstance
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
SemiStructured Data Hisashi Kashima, Teruo Koyanagi

Learning to Fly
by Controlling Dynamic Instabilities David Stirling

Learning
kReversible ContextFree 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 HarPeled, 

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 
NonDisjoint
Discretization for NaiveBayes 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 CostSensitive 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

Semisupervised
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 ModelBased
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 LooNin 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 Boostingbased 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 bottomup constraintbased 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

GraphBased
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 ShienShin
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
ShaweTaylor, Jaz Kandola 
Modeling for
Optimal Probability Prediction Yong Wang, Ian H. Witten

AlgorithmDirected
Exploration for ModelBased Reinforcement Learning Carlos
Guestrin, Relu Patrascu, Dale
Schuurmans 
3:00pm 
Learning the
Kernel Matrix with SemiDefinite 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, ByoungTak Zhang 
4:30pm 
Learning from
Scarce Experience Leonid
Peshkin, Christian Shelton 
TransformationBased
Regression Bjorn
Bringmann, Stefan Kramer, Friedrich
Neubarth, Hannes Pirker, Gerhard
Widmer 
ContentBased
Image Retrieval Using MultipleInstance Learning Qi Zhang, Wei
Yu, Sally Goldman, Jason Fritts 