Time 
Sunday, 8 October 
Monday, 9 October 
Tuesday, 10 October 
10:0011:00 
Morning Invited Talks 

Andrew Ng
Reinforcement Learning and Apprenticeship Learning for Robotic Control

Ulrich Simon
The Usage of the Spectral Norm in Learning Theory: Some Selected Topics

Gunnar Rätsch
The Solution of SemiInfinite Linear Programs using Boostinglike Methods

11:0011:30 
Coffee Break 
11:3012:20 
Query Learning 
Online Learning 
Reinforcement Learning 

Nader H. Bshouty and Ehab Wattad
On Exact Learning Halfspaces with
Random Consistent Hypothesis Oracle
Gold Award Lecture
Alp Atıcı and Rocco A. Servedio
Learning Unions of ω(1)$Dimensional Rectangles

Nader H. Bshouty and Iddo Bentov
On Exact Learning from Random Walk
Eyal EvenDar, Michael Kearns and Jennifer Wortman
RiskSensitive Online Learning

Daniil Ryabko and Marcus Hutter
Asymptotic Learnability of Reinforcement
Problems with Arbitrary Dependence
Takeshi Shibata, Ryo Yoshinaka and Takashi Chikayama
Probabilistic Generalization of Simple Grammars and Its Application to
Reinforcement Learning

12:2012:30 
Short Break 
12:3013:20 
Applications of Query Learning 
Predicting with Experts 
Statistical Learning 

Matti Kääriäinen
Active Learning in the Nonrealizable Case
Jorge Castro
How Many Query Superpositions Are Needed to Learn?

Vladimir Vovk
Leading Strategies in Competitive OnLine Learning
Chamy Allenberg, Peter Auer, Laszlo Györfi and György Ottucsák
Hannan Consistency in OnLine Learning in Case of Unbounded Losses
under Partial Monitoring

Andreas Maurer
Unsupervised Slow SubspaceLearning from Stationary
Processes
Atsuyoshi Nakamura
LearningRelated Complexity of Linear Ranking Functions

13:3015:00 
Lunch Break 
15:0016:00 
Afternoon Invited Talks 


Carole Goble
Putting Semantics into eScience and the Grid

Padhraic Smyth
DataDriven Discovery using Probabilistic Hidden Variable Models

Adjorn

16:0016:10 
Short Break 

16:1017:25 
Complexity of Learning 
Algorithmic Forecasting 


Frank J. Balbach and Thomas Zeugmann
Teaching Memoryless Randomized Learners without Feedback
Stephen Fenner and William Gasarch
The Complexity of Learning SUBSEQ(A)
Matthew de Brecht and Akihiro Yamamoto
Mind Change Complexity of
Inferring Unbounded Unions of Pattern Languages From Positive Data

Marcus Hutter
General Discounting versus Average Reward
Jan Poland
The Missing Consistency Theorem for Bayesian Learning:
Stochastic Model Selection
Shane Legg
Is there an Elegant Universal Theory of Prediction?


17:2517:55 
Coffee Break 

17:5519:10 
Inductive Inference 
Boosting, Support Vector Machines and Kernel Methods 


Sanjay Jain and Efim Kinber
Learning and Extending Sublanguages
Sanjay Jain and Efim Kinber
Iterative Learning from Positive Data
and Negative Counterexamples
Sanjay Jain, Steffen Lange and Sandra Zilles
Towards a Better Understanding of Incremental Learning

Leonid Kontorovich, Corinna Cortes, Mehryar Mohri
Learning Linearly Separable Languages
Kohei Hatano
Smooth Boosting Using an InformationBased Criterion
HsuanTien Lin and Ling Li
LargeMargin Thresholded Ensembles
for Ordinal Regression: Theory and Practice


20:00 
Business Meeting 
Banquet 
