ICML'99 Program

ICML'99 at a glance

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Saturday, JUN 26

14:00-16:00 Tutorial Session 1
16:00-16:30 Coffee break
16:30-18:30 Tutorial Session 2

20:30 Joint ICML'99 + ILP'99 reception
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Sunday, JUN 27                Monday, JUN 28                Tuesday, JUN 29
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 9:00-9:15                     9:00-10:00                    9:00-10:00
  Opening addresses             Invited 2: Anderson           Invited 3: Cohen
9:15-10:15                    10:00                         10:00
  Invited 1: Quinlan            Coffee break                  Coffee break
10:15                         10:30-12:35                   10:30-12:10
  Coffee break                  Sessions 4a, 4b               Session 6
10:45-12:25             
  Session 1

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  Lunch break                   Lunch break                   Lunch break
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14:00-15:40                   14:00-16:05                   14:00-15:40
  Sessions 2a, 2b               Sessions 5a, 5b               Session 7
15:40                         16:10-16:40                   15:40
  Coffee break                  MLnet community               Coffee break
16:00-18:05                     meeting                     16:00-17:40
  Sessions 3a, 3b             17:00-22:30                     Session 8
                                Trip to Bohinj,             
                                dinner                      17:50-18:20
                                                              Workshops preview 
19:00-21:00                                                 18:20-18:50
  Poster Session                                              ICML community 
  ICML'99 + ILP'99                                            meeting

                                                            20:30
                                                              Concert 
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Wednesday, JUN 30

ICML'99 Workshops
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ICML'99 Session program

Invited Talk 1 (Joint ICML'99 and ILP'99 Invited Talk): Sunday, 9:15 - 10:15

• Some elements of machine learning
  J. Ross Quinlan

Session 1 (Text, Internet): Sunday, 10:45 - 12:25

• Active Learning for Natural Language Parsing and Information Extraction
  Cynthia A. Thompson, Mary Elaine Califf, and Raymond J. Mooney

• Transductive Inference for Text Classification using Support Vector Machines
  Thorsten Joachims

• Learning to Optimally Schedule Internet Banner Advertisements
  Naoki Abe and Atsuyoshi Nakamura

• Using Reinforcement Learning to Spider the Web Efficiently
  Jason Rennie and Andrew Kachites McCallum

Session 2a (Temporal Data, Gesture Recognition): Sunday, 14:00 - 15:40

• Local Learning for Iterated Time Series Prediction
  Gianluca Bontempi, Mauro Birattari, and Hugues Bersini

• A Region-Based Learning Approach to Discovering Temporal Structures in Data
  Wei Zhang

• Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
  Sebastian Thrun, John C. Langford, and Dieter Fox

• Learning Comprehensible Descriptions of Multivariate Time Series
  Mohammed Waleed Kadous

Session 2b (ILP, Applications): Sunday, 14:00 - 15:40

• Noise-Tolerant Recursive Best-First Induction
  Uros Pompe

• Learning Discriminatory and Descriptive Rules by an Inductive Logic Programming System
  Maziar Palhang and Arcot Sowmya

• Learning User Evaluation Functions for Adaptive Scheduling Assistance
  Melinda T. Gervasio, Wayne Iba, and Pat Langley

• Learning Hierarchical Performance Knowledge by Observation
  Michael van Lent and John Laird

Session 3a (Reinforcement Learning, Control): Sunday, 16:00 - 18:05

• Learning Policies with External Memory
  Leonid Peshkin, Nicolas Meuleau, and Leslie Pack Kaelbling

• Distributed Value Functions
  Jeff Schneider, Weng-Keen Wong, Andrew Moore, and Martin Riedmiller

• Associative Reinforcement Learning using Linear Probabilistic Concepts
  Naoki Abe and Philip M. Long

• Efficient Non-Linear Control by Combining Q-learning with Local Linear Controllers
  Hajime Kimura and Shigenobu Kobayashi

• Learning to Ride a Bicycle using Iterated Phantom Induction
  Mark Brodie and Gerald DeJong

Session 3b (Classification, Boosting): Sunday, 16:00 - 18:05

• AdaCost: Misclassification Cost-sensitive Boosting
  Wei Fan, Salvatore J. Stolfo, Junxin Zhang, and Philip K. Chan

• Boosting a Strong Learner: Evidence Against the Minimum Margin
  Michael Harries

• Tractable Average-Case Analysis of Naive Bayesian Classifiers
  Pat Langley and Stephanie Sage

• Correcting Noisy Data
  Choh Man Teng

• A Minimum Risk Metric for Nearest Neighbor Classification
  Enrico Blanzieri and Francesco Ricci

Invited Talk 2: Monday, 9:00 - 10:00

• ACT-R and Learning
  John R. Anderson

Session 4a (Biology and Medicine): Monday, 10:30 - 12:35

• Detecting Motifs from Sequences
  Yuh-Jyh Hu, Suzanne Sandmeyer, and Dennis Kibler

• Hierarchical Models for Screening of Iron Deficiency Anemia
  Igor V. Cadez, Christine E. McLaren, Padhraic Smyth, and Geoffrey J. McLachlan

• Combining statistical learning with a knowledge-based approach -- A case study in intensive care monitoring
  Katharina Morik, Peter Brockhausen, and Thorsten Joachims

• Experiments with noise filtering in a medical domain
  Dragan Gamberger, Nada Lavrac, and Ciril Groselj

• OPT-KD: An Algorithm for Optimizing Kd-Trees
  Doug Talbert and Doug Fisher

Session 4b (Natural Language and Text): Monday, 10:30 - 12:35

• GA-based Learning of Context-Free Grammars using Tabular Representations
  Yasubumi Sakakibara and Mitsuhiro Kondo

• Instance-Family Abstraction in Memory-Based Language Learning
  Antal van den Bosch

• Combining Error-Driven Pruning and Classification for Partial Parsing
  Claire Cardie, Scott Mardis, and David Pierce

• Feature Engineering for Text Classification
  Sam Scott and Stan Matwin

• Feature selection for unbalanced class distribution and Naive Bayes
  Dunja Mladenic and Marko Grobelnik

Session 5a (Feature Selection, Numerical Regression): Monday, 14:00 - 16:05

• Attribute dependencies, understandability and split selection in tree based models
  Marko Robnik-Sikonja and Igor Kononenko

• Making Better Use of Global Discretization
  Eibe Frank and Ian H. Witten

• Feature Selection as a Preprocessing Step for Hierarchical Clustering
  Luis Talavera

• Approximation Via Value Unification
  Paul E. Utgoff and David J. Stracuzzi

• A Hybrid Lazy-Eager Approach to Reducing the Computation and Memory Requirements of Local Parametric Learning Algorithms
  Yuanhui Zhou and Carla Brodley

Session 5b (Miscellaneous): Monday, 14:00 - 16:05

• An Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High-Dimensional Sparse Data
  Marina Meila

• Simple DFA are Polynomially Probably Exactly Learnable from Simple Examples
  Rajesh Parekh and Vasant Honavar

• Expected Error Analysis for Model Selection
  Tobias Scheffer and Thorsten Joachims

• Machine-Learning Applications of Algorithmic Randomness
  Volodya Vovk, Alex Gammerman, and Craig Saunders

• On some misbehaviour of back-propagation with non-normalized RBFNs and a solution
  Attilio Giordana and Roberto Piola

Invited Talk 3: Tuesday, 9:00 - 10:00

• What Can We Learn From the Web
  William W. Cohen

Session 6 (Reinforcement Learning): Tuesday, 10:30 - 12:10

• Least-Squares Temporal Difference Learning
  Justin A. Boyan

• Policy invariance under reward transformations: Theory and application to reward shaping
  Andrew Y. Ng, Daishi Harada, and Stuart Russell

• Hierarchical Optimization of Policy-Coupled Semi-Markov Decision Processes
  Gang Wang and Sridhar Mahadevan

• Implicit Imitation in Multiagent Reinforcement Learning
  Bob Price and Craig Boutilier

Session 7 (Robots, Clustering): Tuesday, 14:00 - 15:40

• Sonar-Based Mapping With Mobile Robots using EM
  Wolfram Burgard, Dieter Fox, Hauke Jans, Christian Matenar, and Sebastian Thrun

• Abstracting from Robot Sensor Data using Hidden Markov Models
  Laura Firoiu and Paul R. Cohen

• Distributed Robotic Learning : Adaptive Behavior Acquisition for Distributed Autonomous Swimming Robot in Real World
  Daisuke Iijima, Wenwei Yu, Hiroshi Yokoi, and Yukinori Kakazu

• Model Selection in Unsupervised Learning With Applications To Document Clustering
  Shivakumar Vaithyanathan and Byron Dom

Session 8 (Decision Trees, Bayes): Tuesday, 16:00 - 17:40

• Large Margin Decision Trees for Induction and Transduction
  Donghui Wu, Kristin P. Bennett, Nello Cristianini, and John Shawe-Taylor

• Discriminant Trees
  Joao Gama

• The alternating decision tree learning algorithm
  Yoav Freund and Llew Mason

• Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique
  Competitive to Boosting Decision Trees Zijian Zheng, Geoffrey I. Webb, and Kai Ming Ting