The 18th International Conference
Algorithmic Learning Theory

Sendai International Center, Sendai, Japan
October 1 - 4, 2007

Time Schedule

The conference starts on Monday, October 1st, with two tutorials, scheduled as follows:

  • 13:00-16:00: Tutorial I
    Marcus Hutter: On the Philosophical, Statistical, and Computational Foundations of Inductive Inference and Intelligent Agents.
  • 16:00-16:30: Coffee Break
  • 16:30-18:00: Tutorial II
    Kazuyuki Tanaka: Introduction to Probabilistic Image Processing and Bayesian Networks.
  • from 17:00: Registration Desk Open
  • from 18:30: Reception

Further information on the tutorials can be found on the Tutorial page.

The table below presents the schedule for the other three days of the conference.

  Tuesday, October 2   Wednesday, October 3   Thursday, October 4
9:20-9:30  Opening        
9:30-10:30 Invited Talk 9:00-10:00 Intived Talk 9:00-10:00 Invited Talk
  Jürgen Schmidhuber
Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity & Creativity
  Thomas G. Dietterich
Machine Learning in Ecosystem Informatics
  Masaru Kitsuregawa
Challenge for Info-plosion
10:30-10:45 Coffee Break 10:00-10:15 Coffee Break 10:00-10:15 Coffee Break
10:45-12:25 Query Learning 10:15-11:30 Online Learning 10:15-11:30 Inductive Inference
  Sanjay Jain and Efim Kinber
One-shot Learners Using Negative Counterexamples and Nearest Positive Examples

Cristina Tîrnăucă and Timo Knuutila
Polynomial Time Algorithms for Learning k-Reversible Languages and Pattern Languages with Correction Queries

Lev Reyzin and Nikhil Srivastava
Learning and Verifying Graphs using Queries with a Focus on Edge Counting

Rika Okada, Satoshi Matsumoto, Tomoyuki Uchida, Yusuke Suzuki, and Takayoshi Shoudai
Exact Learning of Finite Unions of Graph Patterns from Queries
  Jean-Yves Audibert, Rémi Munos, and Csaba Szepesvári
Tuning Bandit Algorithms in Stochastic Environments

Jussi Kujala and Tapio Elomaa
Following the Perturbed Leader to Gamble at Multi-Armed Bandits

Steven Busuttil and Yuri Kalnishkan
Online Regression Competitive with Changing Predictors
  John Case, Timo Kötzing, and Todd Paddock
Feasible Iteration of Feasible Learning Functionals

John Case and Samuel E. Moelius III
Parallelism Increases Iterative Learning Power

Sanjay Jain and Frank Stephan
Learning in Friedberg Numberings
  11:30-11:45 Coffee Break 11:30-11:45 Coffee Break
  11:45-12:35 Online Decision Making 11:45-13:00 Language Learning
    Vladimir V. V'yugin
On Calibration Error of Randomized Forecasting Algorithms

Ronald Ortner Pseudometrics for State
Aggregation in Average Reward Markov Decision Processes
  Sanjay Jain, Frank Stephan, and Nan Ye
Prescribed Learning of R.E. Classes

Ryo Yoshinaka
Learning Efficiency of Very Simple Grammars from Positive Data

François Denis and Amaury Habrard
Learning Rational Stochastic Tree Languages
12:25-13:30 Lunch Break 12:35-14:00 Lunch and Business Meeting   Adjorn
13:30-14:30 Invited Talk 14:00-15:00 Invited Talk    
  Avrim Blum
A Theory of Similarity Functions for Learning and Clustering
  Alexander J. Smola
A Hilbert Space Embedding for Distributions
14:30-14:45 Coffee Break 15:00-15:15 Coffee Break    
14:45-16:00 Unsupervised Learning & Boosting 15:15-16:30 Kernel-Based Learning    
  <Gold Award Lecture> Markus Maier, Matthias Hein, and Ulrike von Luxburg
Cluster Identification in Nearest-Neighbor Graphs

Kevin L. Chang
Multiple pass streaming algorithms for learning mixtures of distributions in Rd

Takafumi Kanamori
Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability
  Kilho Shin and Tetsuji Kuboyama
Polynomial Summaries of Positive Semidefinite Kernels

Guillaume Stempfel and Liva Ralaivola
Learning Kernel Perceptrons on Noisy Data using Random Projections

Adam Kowalczyk
Continuity of Performance Metrics for Thin Feature Maps
16:00-16:25 Coffee Break 16:30-18:30 Short Walk    
16:25-18:05 Complexity Aspects of Learning   Move to the site of Sendai Castle, 20 minutes walk or 5 minutes by Bus. Walking around the site and museum.    
  Vitaly Feldman, Shrenik Shah, and Neal Wadhwa
Separating Models of Learning with Faulty Teachers

César L. Alonso and José Luis Montaña
Vapnik-Chervonenkis Dimension of Parallel Arithmetic Computations

Vikraman Arvind, Johannes Köbler, and Wolfgang Lindner
Parameterized Learnability of k-Juntas and related Problems

M. M. Hassan Mahmud
On Universal Transfer Learning
  18:30- Banquet (Site of Sendai Castle)    
19:00- Joint Steering Committee Meeting        

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