ICML-2000 - Schedule for Talks
Thursday, June 29
9:00 AM
Ensemble Methods (Track 1)
- Bayesian Averaging of Classifiers and the Overfitting Problem
Pedro Domingos
- Bounds on the Generalization Performance of Kernel Machine Ensembles
Theodoros Evgeniou, Luis Perez-Breva, Massimiliano Pontil, and Tomaso Poggio
- A Normative Examination of Ensemble Learning Algorithms
David M. Pennock, Pedrito Maynard-Reid II, C. Lee Giles, and Eric Horvitz
Anomaly Detection (Track 2)
- Local Expert Autoassociators for Anomaly Detection
Geoffrey G. Towell
- Data Reduction Techniques for Instance-Based Learning from Human/Computer Interface Data
Terran Lane and Carla E. Brodley
- Anomaly Detection over Noisy Data using Learned Probability Distributions
Eleazar Eskin
Reinforcement Learning with Continuous States (Track 3)
- Practical Reinforcement Learning in Continuous Spaces
William D. Smart and Leslie Pack Kaelbling
- Combining Reinforcement Learning with a Local Control Algorithm
Jette Randlov, Andrew G. Barto, and Michael T. Rosenstein
- Hidden Strengths and Limitations: An Empirical Investigation of Reinforcement Learning
Gerald DeJong
Supervised Learning (Track 4)
- The Effect of the Input Density Distribution on Kernel-based Classifiers
Christopher K. I. Williams and Matthias Seeger
- Classification with Multiple Latent Variable Models using Maximum Entropy Discrimination
Machiel Westerdijk and Wim Wiegerinck
- Duality and Geometry in SVM Classifiers
Kristin P. Bennett and Erin J. Bredensteiner
10:05 AM Break
10:25 AM Invited Talk (Plenary)
- Predictive Learning through Gradient Boosting
Jerome Friedman
11:45 AM
Online Ensemble Learning (Track 1)
- Selective Voting for Perceptron-like Online Learning
Yi Li
- Online Ensemble Learning: An Empirical Study
Alan Fern and Robert Givan
Regression Learning (Track 2)
- Learning to Predict Performance from Formula Modeling and Training Data
Bryan Singer and Manuela Veloso
- Partial Linear Trees
Luis Torgo
Reinforcement Learning with Continuous States (Track 3)
- Rates of Convergence for Variable Resolution Schemes in Optimal Control
Remi Munos and Andrew W. Moore
- Adaptive Resolution Model-Free Reinforcement Learning: Decision Boundary Partitioning
Stuart I. Reynolds
Knowledge Transfer in Learning (Track 4)
- Empirical Bayes for Learning to Learn
Tom Heskes
- Disciple-COA: From Agent Programming to Agent Teaching
M. Boicu, G. Tecuci, D. Marcu, M. Bowman, P. Shyr, F. Ciucu, and C. Levcovici
12:30 PM Lunch
2:00 PM
Ensemble Methods (Track 1)
- Voting Nearest-Neighbor Subclassifiers
Miroslav Kubat and Martin Cooperson, Jr.
- Lightweight Rule Induction
Sholom M. Weiss and Nitin Indurkhya
- MultiStage Cascading of Multiple Classifiers: One Man's Noise is Another Man's Data
Cenk Kaynak and Ethem Alpaydin
- "Boosting'' a Positive-Data-Only Learner
Andrew Mitchell
Learning from Text (Track 2)
- Approximate Dimension Equalization in Vector-based Information Retrieval
Fan Jiang and Michael L. Littman
- Using Error-Correcting Codes for Text Classification
Rayid Ghani
- Support Vector Machine Active Learning with Applications to Text Classification
Simon Tong and Daphne Koller
- Learning to Select Text Databases with Neural Nets
Yong S. Choi and Suk I. Yoo
Learning Motor Skills (Track 3)
- Behavioral Cloning of Student Pilots with Modular Neural Networks
Charles W. Anderson, Bruce A. Draper, and David A. Peterson
- Learning to Fly: An Application of Hierarchical Reinforcement Learning
Malcolm Ryan and Mark Reid
- On-line Learning for Humanoid Robot Systems
Jorg Conradt, Gaurav Tevatia, Sethu Vijayakumar, and Stefan Schaal
- Acquisition of Stand-up Behavior by a Real Robot using Hierarchical Reinforcement Learning
Jun Morimoto and Kenji Doya
Unsupervised Learning (Track 4)
- Finding Variational Structure in Data by Cross-Entropy Optimization
Matthew Brand
- An Approach to Data Reduction and Clustering with Theoretical Guarantees
Partha Niyogi and Narendra Karmarkar
- X-means: Extending K-means with Efficient Estimation of the Number of Clusters
Dan Pelleg and Andrew Moore
- Model Selection Criteria for Learning Belief Nets: An Empirical Comparison
Tim Van Allen and Russ Greiner
3:30 PM Break
4:00 PM
Learning Structures and Relations (Track 1)
- Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space
Alberto Paccanaro and Geoffrey E. Hinton
- Classification of Individuals with Complex Structure
A. F. Bowers, C. Giraud-Carrier, and J. W. Lloyd
- Data as Ensembles of Records: Representation and Comparison
Nicholas R. Howe
- Learning Horn Expressions with LogAn-H
Roni Khardon
- Ideal Theory Refinement under Object Identity
Floriana Esposito, Nicola Fanizzi, Stefano Ferilli, and Giovanni Semeraro
Learning from Text (Track 2)
- Combining Multiple Learning Strategies for Effective Cross Validation
Yiming Yang, Thomas Ault, and Thomas Pierce
- Detecting Concept Drift with Support Vector Machines
Ralf Klinkenberg and Thorsten Joachims
- Learning to Probabilistically Identify Authoritative Documents
David Cohn and Huan Chang
- Improving Short-Text Classification using Unlabeled Background Knowledge to Assess Document Similarity
Sarah Zelikovitz and Haym Hirsh
- Automatically Extracting Features for Concept Learning from the Web
William W. Cohen
Learning Motor Skills (Track 3)
- An Integrated Connectionist Approach to Reinforcement Learning for Robotic Control
Dean F. Hougen, Maria Gini, and James Slagle
- Learning Multiple Models for Reward Maximization
Dani Goldberg and Maja J. Mataric
- Learning in Non-stationary Conditions: A Control Theoretic Approach
Jefferson Coelho and Rod Grupen
- Locally Weighted Projection Regression: An O(n) Algorithm for Incremental Real Time Learning in High Dimensional Space
Sethu Vijayakumar and Stefan Schaal
Computational Scientific Discovery (Track 4)
- Enhancing the Plausibility of Law Equation Discovery
Takashi Washio, Hiroshi Motoda, and Yuji Niwa
- Discovering the Structure of Partial Differential Equations from Example Behaviour
Ljupco Todorovski, Saso Dzeroski, Ashwin Srinivasan, Jonathan Whiteley, and David Gavaghan
- Automatic Identification of Mathematical Concepts
Simon Colton, Alan Bundy, and Toby Walsh
- A Divide and Conquer Approach to Learning from Prior Knowledge
Eric Chown and Thomas G. Dietterich
7:00 PM Opening Reception - Governor's Corner
Friday, June 30
9:00 AM
Active Learning and Experimentation (Track 1)
- Less is More: Active Learning with Support Vector Machines
Greg Schohn and David Cohn
- Efficient Mining from Large Databases by Query Learning
Hiroshi Mamitsuka and Naoki Abe
- Query Learning with Large Margin Classifiers
Colin Campbell, Nello Cristianini, and Alex Smola
Increasing the Rate of Reinforcement Learning (Track 2)
- Eligibility Traces for Off-Policy Policy Evaluation
Doina Precup, Richard S. Sutton, and Satinder Singh
- Knowledge Propagation in Model-based Reinforcement Learning Tasks
Corinna Richter and Jorg Stachowiak
- Shaping in Reinforcement Learning by Changing the Physics of the Problem
Jette Randlov
Efficiency Issues in Learning (Track 3)
- A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets
Paul Komarek and Andrew Moore
- Dimension Reduction Techniques for Training Polynomial Networks
William M. Campbell, Kari Torkkola, and Sreeram V. Balakrishnan
- Complete Cross-Validation for Nearest Neighbor Classifiers
Matthew Mullin and Rahul Sukthankar
Supervised Learning (Track 4)
- The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms
Jinyan Li, Kotagiri Ramamohanarao, and Guozhu Dong
- Direct Bayes Point Machines
Matthias Rychetsky, John Shawe-Taylor, and Manfred Glesner
- FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness
Joseph O'Sullivan, John Langford, Rich Caruana, and Avrim Blum
10:05 AM Break
10:25 AM Invited Talk (Plenary)
- Learning From and About Users
Haym Hirsh
11:45 AM
Dealing with Noise (Track 1)
- Learning Priorities From Noisy Examples
Geoffrey G. Towell, Thomas Petsche, and Michael R. Miller
- A Nonparametric Approach to Noisy and Costly Optimization
Brigham S. Anderson, Andrew W. Moore, and David Cohn
Increasing the Rate of Reinforcement Learning (Track 2)
- Localizing Policy Gradient Estimates to Action Transitions
Gregory Z. Grudic and Lyle H. Ungar
- A Bayesian Framework for Reinforcement Learning
Malcolm Strens
Efficiency Issues in Learning (Track 3)
- Estimating the Generalization Performance of an SVM Efficiently
Thorsten Joachims
- Sparse Greedy Matrix Approximation for Machine Learning
Alex J. Smola and Bernhard Scholkopf
Supervised Learning (Track 4)
- A Column Generation Algorithm For Boosting
Kristin P. Bennett, Ayhan Demiriz, and John Shawe-Taylor
- Linear Discriminant Trees
Olcay Taner Yildiz and Ethem Alpaydin
12:30 PM Lunch
2:00 PM
Adaptive User Interfaces (Track 1)
- Learning Subjective Functions with Large Margins
Claude-Nicolas Fiechter and Seth Rogers
- Algorithms for Inverse Reinforcement Learning
Andrew Y. Ng and Stuart Russell
- Clustering the Users of Large Web Sites into Communities
Georgios Paliouras, Christos Papatheodorou, Vangelis Karkaletsis, and Constantine Spyropoulos
- Version Space Algebra and its Application to Programming by Demonstration
Tessa Lau, Pedro Domingos, and Daniel S. Weld
Learning in Problem Solving (Track 2)
- Knowledge Representation Issues in Control Knowledge Learning
Ricardo Aler, Daniel Borrajo, and Pedro Isasi
- Learning Declarative Control Rules for Constraint-Based Planning
Yi-Cheng Huang, Bart Selman, and Henry Kautz
- Machine Learning for Subproblem Selection
Robert Moll, Theodore J. Perkins, and Andrew G. Barto
- Achieving Efficient and Cognitively Plausible Learning in Backgammon
Scott Sanner, John R. Anderson, Christian Lebiere, and Marsha Lovett
Empirical Predictions of Learning Behavior (Track 3)
- A Quantification of Distance Bias Between Evaluation Metrics In Classification
Ricardo Vilalta and Daniel Oblinger
- Analyzing Relational Learning in the Phase Transition Framework
Attilio Giordana, Lorenza Saitta, Michele Sebag, and Marco Botta
- Meta-Learning by Landmarking Various Learning Algorithms
Bernhard Pfahringer, Hilan Bensusan, and Christophe Giraud-Carrier
- Characterizing Model Errors and Differences
Stephen D. Bay and Michael J. Pazzani
Semi-supervised Learning (Track 4)
- Discovering Test Set Regularities in Relational Domains
Sean Slattery and Tom Mitchell
- A Probability Analysis on the Value of Unlabeled Data for Classification Problems
Tong Zhang and Frank J. Oles
- An Adaptive Regularization Criterion for Supervised Learning
Dale Schuurmans and Finnegan Southey
- Enhancing Supervised Learning with Unlabeled Data
Sally Goldman and Yan Zhou
3:30 PM Break
4:00 PM
Adaptive User Interfaces (Track 1)
- Incremental Learning in SwiftFile
Richard B. Segal and Jeffrey O. Kephart
- Learning to Create Customized Authority Lists
Huan Chang, David Cohn, and Andrew K. McCallum
- Challenges of the Email Domain for Text Classification
Jake D. Brutlag and Christopher Meek
- State-based Classification of Finger Gestures from Electromyographic Signals
Peter Ju, Leslie Pack Kaelbling, and Yoram Singer
Learning Plans and Programs (Track 2)
- Learning Probabilistic Models for Decision-Theoretic Navigation of Mobile Robots
Daniel Nikovski and Illah Nourbakhsh
- Learning Through Evolution: Neural Programming and Internal Reinforcement
Astro Teller and Manuela Veloso
- Reinforcement Learning in POMDP's via Direct Gradient Ascent
Jonathan Baxter and Peter L. Bartlett
- Algorithm Selection using Reinforcement Learning
Michail G. Lagoudakis and Michael L. Littman
Theoretical Predictions of Learning Behavior (Track 3)
- A Unified Bias-Variance Decomposition and its Applications
Pedro Domingos
- Generalized Average-Case Analyses of the Nearest Neighbor Algorithm
Seishi Okamoto and Nobuhiro Yugami
- Why Discretization Works for Naive Bayesian Classifiers
Chun-Nan Hsu, Hung-Ju Huang, and Tzu-Tsung Wong
- Predicting the Generalization Performance of Cross Validatory Model Selection Criteria
Tobias Scheffer
Unsupervised Learning (Track 4)
- Clustering with Instance-level Constraints
Kiri Wagstaff and Claire Cardie
- Hierarchical Unsupervised Learning
Shivakumar Vaithyanathan and Byron Dom
- A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models
Cen Li and Gautam Biswas
- Mixtures of Factor Analyzers
Geoffrey McLachlan and David Peel
7:00 PM Poster Reception - Governor's Corner
Saturday, July 1
9:00 AM
Feature and Instance Selection in Supervised Learning (Track 1)
- Selection of Support Vector Kernel Parameters for Improved Generalization
Loo-Nin Teow and Kia-Fock Loe
- Instance Pruning as an Information Preserving Problem
Marc Sebban and Richard Nock
- Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
Mark A. Hall
Analyses of Reinforcement Learning (Track 2)
- Fixed Points of Approximate Value Iteration and Temporal-Difference Learning
Daniela Pucci de Farias and Benjamin Van Roy
- Relative Loss Bounds for Temporal-Difference Learning
Jurgen Forster and Manfred Warmuth
- A Universal Generalization for Temporal-Difference Learning Using Haar Basis Functions
Susumu Katayama, Hajime Kimura, and Shigenobu Kobayashi
Cost-Sensitive Learning (Track 3)
- A Comparative Study of Cost-Sensitive Boosting Algorithms
Kai Ming Ting
- Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers
Dragos D. Margineantu and Thomas G. Dietterich
- Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
Chris Drummond and Robert C. Holte
Natural Language (Track 4)
- Discriminative Reranking for Natural Language Parsing
Michael Collins
- Maximum Entropy Markov Models for Information Extraction and Segmentation
Andrew McCallum, Dayne Freitag, and Fernando Pereira
- Meta-Learning for Phonemic Annotation of Corpora
Veronique Hoste, Walter Daelemans, Erik Tjong Kim Sang, and Steven Gillis
10:05 AM Break
10:25 AM Invited Talk (Plenary)
- Feature Construction
Paul Utgoff
11:45 AM
Feature Selection in Unsupervised Learning (Track 1)
- Feature Selection and Incremental Learning of Probabilistic Concept Hierarchies
Luis Talavera
- Feature Subset Selection and Order Identification for Unsupervised Learning
Jennifer G. Dy and Carla E. Brodley
Multi-agent Learning (Track 2)
- Combining Multiple Perspectives
Bikramit Banerjee, Sandip Debnath, and Sandip Sen
- Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions
Manu Sridharan and Gerald Tesauro
Supervised Learning (Track 3)
- Solving the Multiple-Instance Problem: A Lazy Learning Approach
Jun Wang and Jean-Daniel Zucker
- Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
Erin L. Allwein, Robert E. Schapire, and Yoram Singer
Natural Language (Track 4)
- Unpacking Multi-valued Symbolic Features and Classes in Memory-Based Language Learning
Antal van den Bosch and Jakub Zavrel
- Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval
Keith Hall and Thomas Hofmann
12:30 PM Lunch
2:00 PM
Feature Construction (Track 1)
- Induction of Concept Hierarchies from Noisy Data
Blaz Zupan, Ivan Bratko, Marko Bohanec, and Janez Demsar
- Mutual Information in Learning Feature Transformations
Kari Torkkola and William M. Campbell
- Using Knowledge to Speed Learning: A Comparison of Knowledge-based Cascade-correlation and Multi-task Learning
Thomas R. Shultz and Francois Rivest
Multi-Agent Learning in Cooperative Settings (Track 2)
- Multi-Agent Reinforcement Learning for Traffic Light Control
Marco Wiering
- TPOT-RL Applied to Network Routing
Peter Stone
- An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
Martin Lauer and Martin Riedmiller
Computer Vision (Track 3)
- Using Learning by Discovery to Segment Remotely Sensed Images
Leen-Kiat Soh and Costas Tsatsoulis
- Discovering Homogeneous Regions in Spatial Data through Competition
Slobodan Vucetic and Zoran Obradovic
- Image Color Constancy Using EM and Cached Statistics
Charles Rosenberg
Natural Language (Track 4)
- Using Natural Language Processing and Discourse Features to Identify Understanding Errors in a Spoken Dialogue System
Marilyn Walker, Jerry Wright, and Irene Langkilde
- Comparing the Minimum Description Length Principle and Boosting in the Automatic Analysis of Discourse
Tadashi Nomoto and Yuji Matsumoto
- A Boosting Approach to Topic Spotting on Subdialogues
Kary Myers, Michael Kearns, Satinder Singh, and Marilyn A. Walker
3:05 PM Break
3:30 PM
Scientific Applications of Machine Learning (Track 1)
- Learning Filaments
Geoffrey J. Gordon and Andrew Moore
- Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes
Mark Craven, David Page, Jude Shavlik, Joseph Bockhorst, and Jeremy Glasner
- Learning Chomsky-like Grammars for Biological Sequence Families
S. H. Muggleton, C. H. Bryant, and A. Srinivasan
Multi-Agent Learning in Competitive Settings (Track 2)
- Pseudo-convergent Q-Learning by Competitive Pricebots
Jeffrey O. Kephart and Gerald J. Tesauro
- Convergence Problems of General-Sum Multiagent Reinforcement Learning
Michael Bowling
- Experimental Results on Q-Learning for General-Sum Stochastic Games
Junling Hu and Michael P. Wellman
Computer Vision (Track 3)
- An Initial Study of an Adaptive Hierarchical Vision System
Marcus A. Maloof
- Learning Bayesian Networks for Diverse and Varying Numbers of Evidence Sets
ZuWhan Kim and Ramakant Nevatia
- Constructive Feature Learning and the Development of Visual Expertise
Justus H. Piater and Roderic A. Grupen
Natural Language (Track 4)
- Bootstrapping Syntax and Recursion using Alignment-Based Learning
Menno van Zaanen
- An Evolutionary Approach to Evidence-Based Learning of Deterministic Finite Automata
Stefan Veeser
- Probabilistic DFA Inference using Kullback-Leibler Divergence and Minimality
Franck Thollard, Pierre Dupont, and Colin de la Higuera
4:45 PM Panel (Plenary)
- The Best and Worst of Twenty Years' Machine Learning Research
5:30 PM ICML Business Meeting (Plenary)
7:00 PM Poster Reception - Governor's Corner