Accepted Papers

ICML 2005 Conference

Accepted Papers to the ICML2005
Exploration and Apprenticeship Learning in Reinforcement Learning Pieter Abbeel, Andrew Y. Ng
Active Learning for Hidden Markov Models: Objective Functions and Algorithms Brigham Anderson, Andrew Moore
Tempering for Bayesian C&RT Nicos Angelopoulos, James Cussens
Fast Condensed Nearest Neighbor Rule Fabrizio Angiulli
Predictive low-rank decomposition for kernel methods Francis R. Bach, Michael I. Jordan
Multi-Way Distributional Clustering via Pairwise Interactions Ron Bekkerman, Ran El-Yaniv, Andrew McCallum
Error Limiting Reductions Between Classification Tasks Alina Beygelzimer, Varsha Dani, Tom Hayes, John Langford, Bianca Zadrozny
Multi-Instance Tree Learning Hendrik Blockeel, David Page, Ashwin Srinivasan
Action Respecting Embedding Michael Bowling, Ali Ghodsi, Dana Wilkinson
Clustering Through Ranking On Manifolds Markus Breitenbach, Gregory Z. Grudic
Reducing Overfitting in Process Model Induction Will Bridewell, Narges Asani, Pat Langley, Ljupco Todorovski
Learning to Rank using Gradient Descent Chris Burges,Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, Greg Hullender
Learning Class-Discriminative Dynamic Bayesian Networks John Burge, Terran Lane
Recognition and Reproduction of Gestures using a Probabilistic Framework combining PCA, ICA and HMM Sylvain Calinon, Aude Billard
Predicting Probability Distributions for Surf Height Using an Ensemble of Mixture Density Networks Michael Carney, Padraig Cunningham, Jim Dowling, Ciaran Lee
Hedged learning: Regret minimization with learning experts Yu-Han Chang, Leslie Kaelbling
Variational Bayesian Image Modelling Li Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang
Preference Learning with Gaussian Processes Wei Chu, Zoubin Ghahramani
New Approaches to Support Vector Ordinal Regression Wei Chu, S. Sathiya Keerthi
A General Regression Technique for Learning Transductions Corinna Cortes, Mehryar Mohri, Jason Weston
Learning to Compete, Compromise, and Cooperate in Repeated General-Sum Games Jacob W. Crandal, Michael A. Goodrich
Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction Hal Daume III, Daniel Marcu
Multimodal Oriented Discriminant Analysis Fernando De la Torre, Takeo Kanade
A Practical Generalization of Fourier-based Learning Adam Drake, Dan Ventura
Combining Model-Based and Instance-Based Learning for First Order Regression Kurt Driessens, Saso Dzeroski
Reinforcement learning with Gaussian processes Yaakov Engel, Shie Mannor, Ron Meir
Experimental Comparison between Bagging and Monte Carlo Ensemble Classification Roberto Esposito, Lorenza Saitta
Supervised Clustering with Support Vector Machines Thomas Finley, Thorsten Joachims
Optimal Assignment Kernels For Attributed Molecular Graphs Holger Fröhlich, Jörg Wegner, Florian Sieker, Andreas Zell
Closed-form dual perturb and combine for tree-based models Pierre Geurts, Louis Wehenkel
Hierarchic Bayesian Models for Kernel Learning Mark Girolami, Simon Rogers
Online Feature Selection for Pixel Classification Karen Glocer, Damian Eads, James Theiler
Learning Strategies for Story Comprehension: A Reinforcement Learning Approach Eugene Grois, David C. Wilkins
Near-Optimal Sensor Placements in Gaussian Processes Carlos Guestrin, Andreas Krause, Ajit Pauk Singh
Robust One-Class Clustering Using Hybrid Global and Local Search Gunjan Gupta, Joydeep Ghosh
Statistical and Computational Analysis of Locality Preserving Projection Xiaofei He, Deng Cai, Wanli Min
Intrinsic Dimensionality Estimation of Submanifolds in $R^d$ Matthias Hein, Jean-Yves Audibert
Bayesian Hierarchical Clustering Katherine Heller, Zoubin Ghahramani
Online Learning over Graphs Mark Herbster, Massimiliano Pontil, Lisa Wainer
Adapting Two-Class Classification Methods to Many Class Problems Simon I. Hill, Arnaud Doucet
A Martingale Framework for Concept Change Detection in Time-Varying Data Streams Shen-Shyang Ho
Multi-Class protein fold detection using adaptive codes Eugene Ie, Jason Weston, William Stafford Noble, Christina Leslie
Learning Approximate Preconditions for Methods in Hierarchical Plans Okhtay Ilghami, Hector Munoz-Avila, Dana S. Nau, David W. Aha
Evaluating Machine Learning for Information Extraction Neil Ireson, Fabio Ciravegna, Mary Elaine Califf, Dayne Freitag, Nicholas Kushmerick, Alberto Lavelli
Learn to Weight Terms in Information Retrieval Using Category Information Rong Jin, Joyce Y. Chai, Luo Si
A Smoothed Boosting Algorithm Using Probabilistic Output Codes Rong Jin, Jian Zhang
Efficient discriminative learning of Bayesian network classifier via Boosted Augmented Naive Bayes Yushi Jing, Vladimir Pavlovic, James M. Rehg
A Support Vector Method for Multivariate Performance Measures Thorsten Joachims
Error Bounds for Correlation Clustering Thorsten Joachims, John Hopcroft
Interactive Learning of Mappings from Visual Percepts to Actions Sébastien Jodogne, Justus H. Piater
A Causal Approach to Hierarchical Decomposition of Factored MDPs Anders Jonsson, Andrew Barto
A Comparison of Tight Generalization Error Bounds Matti Kääriäinen, John Langford
Generalized LARS as an Effective Feature Selection Tool for Text Classification With SVMs S. Sathiya Keerthi
Ensembles of Biased Classifiers Rinat Khoussainov, Andreas Hess, Nicholas Kushmerick
Computational Aspects of Bayesian Partition Models Mikko Koivisto, Kismat Sood
Learning the Structure of Markov Logic Networks Stanley Kok, Pedro Domingos
Using Additive Expert Ensembles to Cope with Concept Drift Jeremy Kolter, Marcus Maloof
Semi-supervised Graph Clustering: A Kernel Approach Brian Kulis, Sugato Basu, Inderjit Dhillon, Raymond Mooney
A Brain Computer Interface with Online Feedback based on Magnetoencephalography Thomas Navin Lal, Michael Schröder, N. Jeremy Hill, Hubert Preissl, Thilo Hinterberger, Juergen Mellinger, Martin Bogdan, Wolfgang Rosenstiel, Niels Birbaumer, Bernhard Schölkopf
Relating Reinforcement Learning Performance to Classification Performance John Langford, Bianca Zadrozny
PAC-Bayes Risk Bounds for Sample-Compressed Gibbs Classifiers François Laviolette, Mario Marchand
Heteroscedastic Gaussian Process Regression Quoc V. Le, Alex J. Smola, Stephane Canu
Predicting Relative Performance of Classifiers from Samples Rui Leite, Pavel Brazdil
Logistic Regression with an Auxiliary Data Source Xuejun Liao, Ya Xue, Lawrence Carin
Predicting Protein Folds with Structural Repeats Using a Chain Graph Model Yan Liu, Eric Xing, Jaime Carbonell
Unsupervised Evidence Integration Philip M. Long, Vinay Varadan, Sarah Gilman, Mark Treshock, Rocco A. Servedio
Naive Bayes Models for Probability Estimation Daniel Lowd, Pedro Domingos
ROC Confidence Bands : An Empirical Evaluation Sofus A. Macskassy, Foster Provost, Saharon Rosset
Modeling Word Burstiness Using the Dirichlet Distribution Rasmus Madsen, David Kauchak, Charles Elkan
Proto-Value Functions: Developmental Reinforcement Learning Sridhar Mahadeva
The cross entropy method for classification Shie Mannor, Dori Peleg, Reuven Rubinstein
Bounded Real-Time Dynamic Programming: RTDP with monotone upper bounds and performance guarantees H. Brendan McMahan, Maxim Likhachev, Geoffrey J. Gordon
Comparing Clusterings - An Axiomatic View Marina Meila
Weighted Decomposition Kernels Sauro Menchetti, Fabrizio Costa, Paolo Frasconi
High Speed Obstacle Avoidance using Monocular Vision and Reinforcement learning Jeff Michels, Ashutosh Saxena, Andrew Y. Ng
Dynamic Preferences in Multi-Criteria Reinforcement Learning Sriraam Natarajan, Prasad Tadepalli
Learning First-Order Probabilistic Models with Combining Rules Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo Restificar
An Efficient Method for Simplifying Support Vector Machines DucDung Nguyen, TuBao Ho
Predicting Good Probabilities With Supervised Learning Alexandru Niculescu-Mizil, Rich Caruana
Recycling Data for Multi-Agent Learning Santi Ontanon, Enric Plaza
A Graphical Model for Chord Progressions Embedded in a Psychoacoustic Space Jean-Francois Paiement, Douglas Eck, Samy Bengio, David Barber
Q-Learning of Sequential Attention for Visual Object Recognition from Informative Local Descriptors Lucas Paletta, Gerald Fritz, Christin Seifert
Discriminative versus Generative Parameter and Structure Learning of Bayesian Network Classifiers Franz Pernkopf, Jeff Bilmes
Optimizing Abstaining Classifiers using ROC Analysis Tadeusz Pietraszek
Independent Subspace Analysis Using Geodesic Spanning Trees Barnabas Poczos, Andras Lorincz
A Model for Handling Approximate, Noisy or Incomplete Labeling in Text Classification Ganesh Ramakrishnan, Krishna Prasad Chitrapura, Raghu Krishnapuram, Pushpak Bhattacharyya
Healing the Relevance Vector Machine through Augmentation Carl Edward Rasmussen, Joaquin Quinonero-Candela
Supervised versus Multiple Instance Learning: An Empirical Comparison Soumya Ray, Mark Craven
Generalized Skewing for Functions with Continuous and Nominal Attributes Soumya Ray, David Page
Fast Maximum Margin Matrix Factorization for Collaborative Prediction Jason D. M. Rennie, Nati Srebro
Coarticulation: An Approach for Generating Concurrent Plans in Markov Decision Processes Khashayar Rohanimanesh, Sridhar Mahadevan
Why Skewing Works: Learning Difficult Boolean Functions with Greedy Tree Learners Bernard Rosell, Lisa Hellerstein, Soumya Ray, David Page
Integer Linear Programming Inference for Conditional Random Fields Dan Roth, Wen-tau Yih
Learning Hierarchical Multi-Category Text Classification Models Juho Rousu, Craig Saunders, Sandor Szedmak, John Shawe-Taylor
Expectation Maximization Algorithms for Conditional Likelihoods Jarkko Salojärvi, Kai Puolamäki, Samuel Kaski
Estimating and computing density based distance metrics Sajama, Alon Orlitsky
Supervised dimensionality reduction using mixture models Sajama, Alon Orlitsky
Object Correspondence as a Machine Learning Problem Bernhard Schölkopf, Florian Steinke, Volker Blanz
Analysis and Extension of Spectral Methods for Nonlinear Dimensionality Reduction Fei Sha, Lawrence K. Saul
Non-Negative Tensor Factorization with Applications to Statistics and Computer Vision Amnon Shashua, Tamir Hazan
Fast Inference and Learning in Large-State-Space HMMs Sajid M. Siddiqi, Andrew W. Moore
New D-Separation Identification Results for Learning Continuous Latent Variable Models Ricardo Silva, Richard Scheines
Identifying Useful Subgoals in Reinforcement Learning by Local Graph Partitioning Ozgur Simsek, Alicia Wolfe, Andrew Barto
Beyond the Point Cloud: from Transductive to Semi-supervised Learning Vikas Sindhwani, Partha Niyogi, Mikhail Belkin
Active Learning for Sampling in Time-Series Experiments With Application to Gene Expression Analysis Rohit Singh, Nathan Palmer, David Gifford, Bonnie Berger, Ziv Bar-Joseph
Compact approximations to Bayesian predictive distributions Edward Snelson, Zoubin Ghahramani
Large Scale Genomic Sequence SVM Classifiers Sören Sonnenburg, Gunnar Rätsch, Bernhard Schölkopf
A Theoretical Analysis of Model-Based Interval Estimation Alexander L. Strehl, Michael L. Littman
Explanation-Augmented SVM: an Approach to Incorporating Domain Knowledge into SVM Learning Qiang Sun, Gerald DeJong
Unifying the Error-Correcting and Output-Code AdaBoost within the Margin Framework Yijun Sun, Sinisa Todorovic, Jian Li, Dapeng Wu
Fite Time Bounds for Sampling Based Fitted Value Iteration Csaba Szepesvari, Remi Munos
TD(lambda) Networks: Temporal-Difference Networks with Eligibility Traces Brian Tanner, Richard Sutton
Learning Structured Prediction Models: A Large Margin Approach Ben Taskar, Vassil Chatalbashev, Daphne Koller, Carlos Guestrin
Learning Discontinuities with Products-of-Sigmoids for Switching between Local Models Marc Toussaint, Sethu Vijayakumar
Core Vector Regression for Very Large Regression Problems Ivor W. Tsang, James T. Kwok, Kimo T. Lai
Propagating Distributions on a Hypergraph by Dual Information Regularization Koji Tsuda
Hierarchical Dirichlet Model for Document Classification Sriharsha Veeramachaneni, Diego Sona, Paolo Avesani
Implicit Surface Modelling as an Eigenvalue Problem Christian Walder, Olivier Chapelle, Bernhard Schölkopf
New Kernels for Protein Structural Motif Discovery and Function Classification Chang Wang, Stephen Scott
Exploiting Syntactic, Semantic and Lexical Regularities in Language Modeling via Directed Markov Random Fields Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng
Bayesian Sparse Sampling for On-line Reward Optimization Tao Wang, Daniel Lizotte, Michael Bowling, Dale Schuurmans
Learning Predictive Representations from a History Eric Wiewiora
Incomplete-Data Classification using Logistic Regression David Williams, Xuejun Liao, Ya Xue, Lawrence Carin
Learning Predictive State Representations in Dynamical Systems Without Reset Britton Wolfe, Michael R. James, Satinder Singh
Linear Asymmetric Classifier for Cascade Detectors Jianxin Wu, Matthew D. Mullin, James M. Rehg
Building Sparse Large Margin Classifiers Mingrui Wu, Bernhard Schölkopf, Goekhan Bakir
Dirichlet Enhanced Relational Learning Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, Hans-Peter Kriegel
Learning Gaussian Processes from Multiple Tasks Kai Yu, Volker Tresp, Anton Schwaighofer
Augmenting Naive Bayes for Ranking Harry Zhang, Liangxiao Jiang, Jiang Su
A New Mallows Distance Based Metric For Comparing Clusterings Ding Zhou, Jia Li, Hongyuan Zha
Learning from Labeled and Unlabeled Data on a Directed Graph Dengyong Zhou, Jiayuan Huang, Bernhard Schölkopf
2D Conditional Random Fields for Web Information Extraction Jun Zhu, Zaiqing Nie, Ji-Rong Wen, Bo Zhang, Wei-Ying Ma
Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning Xiaojin Zhu, John Lafferty
Large Margin Non-Linear Embedding Alexander Zien, Joaquin Quinonero-Candela