ICML-2001
Schedule for Talks
Friday, June 29
9:15 - 10:30 AM Session 1
Invited Talk (Plenary)
- A Guided Tour of Finite Mixture Models: From Pearson to the Web
Padhraic Smyth
10:30 - 11:00 AM Break
11:00 - 12:15 PM Session 2
Applications of Reinforcement Learning/Robot Learning (Track 1)
- Using EM to Learn 3D Models of Indoor Environments with Mobile
Robots
Yufeng Liu, Rosemary Emery, Deepayan Chakrabarti,
Wolfram Burgard, and Sebastian Thrun
- Scaling Reinforcement Learning toward RoboCup Soccer
Peter Stone and Richard Sutton
- Average-Reward Reinforcement Learning for Variance Penalized
Markov Decision Problems
Makoto Sato and Shigenobu Kobayashi
Ensemble Methods (Track 2)
- Round Robin Rule Learning
Johannes Fürnkranz
- An Improved Predictive Accuracy Bound for Averaging Classifiers
John Langford, Matthias Seeger and Nimrod Megiddo
- Convergence Rates of the Voting Gibbs Classifier, with Application
to Bayesian Feature Selection
Andrew Ng and Michael Jordan
NLP/Information Extraction (Track 3)
- A Theory-Refinement Approach to Information Extraction
Tina Eliassi-Rad and Jude Shavlik
- Improving Probabilistic Grammatical Inference Core Algorithms with
Post-Processing Techniques
Franck Thollard
- A Procedure for Unsupervised Lexicon Learning
Anand Venkataraman
12:15 - 1:45 PM Lunch Break
1:45 - 3:25 PM Session 3
MultiAgent Learning (Track 1)
- Learning an Agent's Utility Function by Observing Behavior
Urszula Chajewska, Daphne Koller and Dirk Ormoneit
- Friend-or-Foe Q-learning in General-Sum Games
Michael Littman
- Convergence of Gradient Dynamics with a Variable Learning Rate
Michael Bowling and Manuela Veloso
- On No-Regret Learning, Fictitious Play, and Nash Equilibrium
Amir Jafari, Amy Greenwald, David Gondek and Gunes Ercal
Unsupervised Learning (Track 2)
- Clustering Continuous Time Series
Paola Sebastiani and Marco Ramoni
- Constrained K-means Clustering with Background Knowledge
Kiri Wagstaff, Claire Cardie, Seth Rogers and Stefan Schroedl
- A General Method for Scaling Up Machine Learning Algorithms and its
Application to Clustering
Pedro Domingos and Geoff Hulten
- Repairing Faulty Mixture Models using Density Estimation
Peter Sand and Andrew Moore
3:25 - 4:00 PM Break
4:00 - 5:15 PM Session 4
Reinforcement Learning: The Explore/Exploit Tradeoff (Track 1)
- Exploration Control in Reinforcement Learning using Optimistic
Model Selection
Jeremy Wyatt
- Reinforcement Learning with Bounded Risk
Peter Geibel
- Lyapunov-Constrained Action Sets for Reinforcement Learning
Theodore Perkins and Andrew Barto
Hypertext Categorization and Information Retrieval (Track 2)
- Coupled Clustering: a Method for Detecting Structural Correspondence
Zvika Marx, Ido Dagan, and Joachim Buhmann
- Hypertext Categorization using Hyperlink Patterns and Meta Data
Rayid Ghani, Sean Slattery, Yiming Yang
- Learning to Select Good Title Words: A New Approach based on
Reverse Information Retrieval
Rong Jin and Alexander G. Hauptmann
Multiple Instance Learning/Hypothesis Selection (Track 3)
- Multiple Instance Regression
Soumya Ray and David Page
- Multiple-Instance Learning of Real-Valued Data
Robert Amar, Daniel Dooly, Sally Goldman and Qi Zhang
li> Pairwise Comparison of Hypotheses in Evolutionary Learning
Krzysztof Krawiec
7:00 - 9:30 PM Poster Session/Reception
Saturday, June 30
9:00 - 10:05 AM Session 5
Invited Talk (Plenary)
- Learning Structure from Sequences,
with Applications in a Digital Library
Ian Witten
10:05- 10:30 AM Session 6
Unsupervised Learning (Track 1)
- Mixtures of Rectangles: Interpretable Soft Clustering
Dan Pelleg and Andrew Moore
Function Selection (Track 2)
- Learning to Generate Fast Signal Processing Implementations
Brian Singer and Manuela Veloso
Bayesian Learning (Track 3)
- Collaborative Learning and Recommender Systems
Wee Sun Lee
10:30 - 11:00 AM Break
11:00 - 12:15 PM Session 7
Bayesian Learning (Track 1)
- Bayesian Approaches to Failure Prediction for Disk Drives
Greg Hamerly and Charles Elkan
- WBC-SVM: Weighted Bayesian Classification based on Support Vector
Machines
Thomas Gärtner and Peter Flach
- Learnability of Augmented Naive Bayes in Nominal Domains
Huajie Zhang and Charles Ling
Supervised Learning/Applications (Track 2)
- Visual Development and the Acquisition of Binocular Disparity
Sensitivities
Melissa Dominguez and Robert Jacobs
- Adjusting the Outputs of a Classifier to New a Priori Probabilities
May Significantly Improve Classification Accuracy: Evidence from a
Multi-Class problem in Remote Sensing
Patrice Latinne,Marco Saerens, and Christine Decaestecker
- Discovering Communicable Scientific Knowledge from Spatio-Temporal Data
Mark Schwabacher and Pat Langley
Active Sampling/Kernel Density Estimation (Track 3)
- Incremental Maximization of Non-Instance-Averaging Utility Functions
with Applications to Knowledge Discovery Problems
Tobias Scheffer and Stefan Wrobel
- Toward Optimal Active Learning through Sampling Estimation of
Error Reduction
Nick Roy and Andrew McCallum
- An Efficient Approach for Approximating Multi-Dimensional Range
Queries and Nearest Neighbor Classification in Large Datasets
Carlotta Domeniconi and Dimitrios Gunopulos
12:15 - 1:45 PM Lunch Break
1:45 - 3:25 PM Session 8
Structure in Reinforcement Learning (Track 1)
- Automatic Discovery of Subgoals in Reinforcement Learning using
Diverse Density
Amy McGovern and Andrew G. Barto
- Continuous-Time Hierarchical Reinforcement Learning
Mohammad Ghavamzadeh and Sridhar Mahadevan
- Structured Prioritised Sweeping
Richard Dearden
- Symmetry in Markov Decision Processes and its Implications for
Single Agent and Multiagent Learning
Martin Zinkevich and Tucker Balch
Supervised Learning/Applications (Track 2)
- Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
Neil Lawrence and Bernhard Schoelkopf
- Learning from Labeled and Unlabeled Data using Graph Mincuts
Avrim Blum and Shuchi Chawla
- Obtaining Calibrated Probability Estimates from Decision Trees
and Naive Bayes Classifiers
Bianca Zadrozny and Charles Elkan
- Smoothed Bootstrap and Statistical Data Cloning for Classifier
Evaluation
Gregory Shakhnarovich, Ran El-Yaniv and Yoram Baram
3:25 - 4:00 PM Break
4:00 - 5:15 PM Session 9
SVM and Regression (Track 1)
- A Unified Loss Function in Bayesian Framework for Support Vector
Regression
Wei Chu, S. Sathiya Keerthi and Chong Jin Ong
- Some Sparse Approximation Bounds for Regression Problems
Tong Zhang
- Some Greedy Algorithms for Sparse Nonlinear Regression
Prasanth Nair, Arindam Choudhury and Andy Keane
Learning in Relational Representations/ Reinforcement Learning (Track 2)
- Feature Construction with Version Spaces for Biochemical Applications
Stefan Kramer and Luc De Raedt
- Learning Probabilistic Models of Relational Structure
Lise Getoor, Nir Friedman, Daphne Koller and Benjamin Taskar
- Reinforcement Learning in Dynamic Environments using Instantiated
Information
Marco Wiering
Feature Selection (Track 3)
- Comprehensible Interpretation of Relief's Estimates
Marko Robnik Sikonja and Igor Kononenko
- Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection
Sanmay Das
- Feature Selection for High-Dimensional Genomic Microarray Data
Eric Xing, Michael Jordan and Richard Karp
7:00 - 9:30 PM Poster Session/Reception
Sunday, July 1
9:00 - 10:05 AM Session 10
Invited Talk (Plenary)
- Successes, Failures, and New Directions in Natural Language Learning
Claire Cardie
10:05 - 10:30 AM Session 11
Reinforcement Learning (Track 1)
- Learning Embedded Maps of Markov Processes
Yaakov Engel and Shie Mannor
Unsupervised Learning (Track 2)
- Expectation Maximization for Weakly Labeled Data
Yuri Ivanov, Bruce Blumberg and Alex Pentland
10:30 - 11:00 AM Break
11:00 - 12:15 PM Session 12:
Reinforcement Learning: Searching in Policy Space (Track 1)
- Direct Policy Search using Paired Statistical Tests
Malcolm Strens and Andrew Moore
- A Multi-Agent, Policy Gradient Approach to Network Routing
Nigel Tao, Jonathan Baxter and Lex Weaver
- Evolutionary Search, Stochastic Policies with Memory, and
Reinforcement Learning with Hidden State
Matthew Glickman and Katia Sycara
Decision Trees (Track 2)
- Breeding Decision Trees Using Evolutionary Techniques
Athanasios Papagelis and Dimitrios Kalles
- Efficient Algorithms for Decision Tree Cross-Validation
Hendrik Blockeel and Jan Struyf
- Bias Correction in Classification Tree Construction
Alin Dobra and Johannes Gehrke
IR and SVM (Track 3)
- Relevance Feedback using Support Vector Machines
Harris Drucker, Behzad Shahrary and David C. Gibbon
- Latent Semantic Kernels
Nello Cristianini, John Shawe-Taylor and Huma Lodhi
- Composite Kernels for Hypertext Categorisation
Thorsten Joachims, Nello Cristianini and John Shawe-Taylor
12:15 - 1:45 PM Lunch Break
1:45 - 3:25 PM Session 13
Sequence Learning (Track 1)
- General Loss Bounds for Universal Sequence Prediction
Marcus Hutter
- Application of Fuzzy Similarity-Based Fractal Dimensions to
Characterize Medical Time Series
Manish Sarkar and Tze-Yun Leong
- Unsupervised Sequence Segmentation by a Mixture of Switching
Variable Memory Markov Sources
Yevgeny Seldin, Gill Bejerano and Naftali Tishby
- Conditional Random Fields: Probabilistic Models for Segmenting
and Labeling Sequence Data
John Lafferty, Andrew McCallum and Fernando Pereira
Boosting (Track 2)
- Boosting Neighborhood-Based Classifiers
Marc Sebban, Richard Nock and Stephane Lallich
- Some Theoretical Aspects of Boosting in the Presence of Noisy Data
Wenxin Jiang
- Boosting Noisy Data
Abraham Wyner, Abba Kriege and Chuan Long
- Boosting with Confidence Information
Craig Codrington
3:25 - 4:00 PM Break
4:00 - 4:50 PM Session 14
Temporal Difference Learning/Stochastic algorithms (Track 1)
- A Generalized Kalman Filter for Fixed Point Approximation and
Efficient Temporal Difference Learning
David Choi and Benjamin Van Roy
- Off-Policy Temporal Difference Learning with Function
Approximation
Doina Precup and Richard Sutton
Local Learning (Track 2)
- Inducing Partially-Defined Instances with Evolutionary Algorithms
Xavier Llorà and Josep M. Garrell
- Using the Genetic Algorithm to Reduce the Size of a Nearest-Neighbor
Classifier and to Select Relevant Attributes
Antonin Rozsypal and Miroslav Kubat
COLT (Track 3)
- Learning with the Set Covering Machine
Mario Marchand and John Shawe-Taylor
- Ridge Regression Confidence Machine
Ilia Nouretdinov, Tom Melluish and Volodya Vovk
5:00 - 6:00 PM Business Meeting
7:00 - 9:30 PM Poster Session/Reception