The 17th International Conference
Algorithmic Learning Theory

Barcelona, Spain
October 7-10, 2006

Time Schedule

The conference starts on Saturday, October 7th, with two tutorials, scheduled as follows:

  • 10:00-13:30: Morning Tutorial
    Michael May: Geographic and Spatial Data Mining
  • 13:30-15:00: Lunch Break
  • 15:00-18:30: Afternoon Tutorial
    Luis Torgo: Using R for Data Mining and Scientific Discovery
  • 18:30-20:00: Steering Committee Meeting

The table below presents the schedule for the other three days of the conference. Note that further info on tutorials and invited talks can be found on the Tutorial page.

Time Sunday, 8 October Monday, 9 October Tuesday, 10 October
10:00-11:00 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 Semi-Infinite Linear Programs using Boosting-like Methods
11:00-11:30 Coffee Break
11:30-12:20 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 Even-Dar, Michael Kearns and Jennifer Wortman
Risk-Sensitive 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
12:20-12:30 Short Break
12:30-13:20 Applications of Query Learning Predicting with Experts Statistical Learning
Matti Kääriäinen
Active Learning in the Non-realizable Case

Jorge Castro
How Many Query Superpositions Are Needed to Learn?
Vladimir Vovk
Leading Strategies in Competitive On-Line Learning

Chamy Allenberg, Peter Auer, Laszlo Györfi and György Ottucsák
Hannan Consistency in On-Line Learning in Case of Unbounded Losses under Partial Monitoring
Andreas Maurer
Unsupervised Slow Subspace-Learning from Stationary Processes

Atsuyoshi Nakamura
Learning-Related Complexity of Linear Ranking Functions
13:30-15:00 Lunch Break
15:00-16:00 Afternoon Invited Talks
Carole Goble
Putting Semantics into e-Science and the Grid
Padhraic Smyth
Data-Driven Discovery using Probabilistic Hidden Variable Models
16:00-16:10 Short Break
16:10-17:25 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?
17:25-17:55 Coffee Break
17:55-19:10 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 Information-Based Criterion

Hsuan-Tien Lin and Ling Li
Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice
20:00- Business Meeting Banquet