ICML 2005

134 papers

2D Conditional Random Fields for Web Information Extraction Jun Zhu, Zaiqing Nie, Ji-Rong Wen, Bo Zhang, Wei-Ying Ma

A Brain Computer Interface with Online Feedback Based on Magnetoencephalography Thomas Navin Lal, Michael Schröder, N. Jeremy Hill, Hubert Preißl, Thilo Hinterberger, Jürgen Mellinger, Martin Bogdan, Wolfgang Rosenstiel, Thomas Hofmann, Niels Birbaumer, Bernhard Schölkopf

A Causal Approach to Hierarchical Decomposition of Factored MDPs Anders Jonsson, Andrew G. Barto

A Comparison of Tight Generalization Error Bounds Matti Kääriäinen, John Langford

A General Regression Technique for Learning Transductions Corinna Cortes, Mehryar Mohri, Jason Weston

A Graphical Model for Chord Progressions Embedded in a Psychoacoustic Space Jean-François Paiement, Douglas Eck, Samy Bengio, David Barber

A Martingale Framework for Concept Change Detection in Time-Varying Data Streams Shen-Shyang Ho

A Model for Handling Approximate, Noisy or Incomplete Labeling in Text Classification Ganesh Ramakrishnan, Krishna Prasad Chitrapura, Raghu Krishnapuram, Pushpak Bhattacharyya

A New Mallows Distance Based Metric for Comparing Clusterings Ding Zhou, Jia Li, Hongyuan Zha

A Practical Generalization of Fourier-Based Learning Adam Drake, Dan Ventura

A Smoothed Boosting Algorithm Using Probabilistic Output Codes Rong Jin, Jian Zhang

A Support Vector Method for Multivariate Performance Measures Thorsten Joachims

A Theoretical Analysis of Model-Based Interval Estimation Alexander L. Strehl, Michael L. Littman

Action Respecting Embedding Michael H. Bowling, Ali Ghodsi, Dana F. Wilkinson

Active Learning for Hidden Markov Models: Objective Functions and Algorithms Brigham S. Anderson, Andrew Moore

Active Learning for Sampling in Time-Series Experiments with Application to Gene Expression Analysis Rohit Singh, Nathan P. Palmer, David K. Gifford, Bonnie Berger, Ziv Bar-Joseph

Adapting Two-Class Support Vector Classification Methods to Many Class Problems Simon I. Hill, Arnaud Doucet

An Efficient Method for Simplifying Support Vector Machines DucDung Nguyen, Tu Bao Ho

Analysis and Extension of Spectral Methods for Nonlinear Dimensionality Reduction Fei Sha, Lawrence K. Saul

Augmenting Naive Bayes for Ranking Harry Zhang, Liangxiao Jiang, Jiang Su

Bayesian Hierarchical Clustering Katherine A. Heller, Zoubin Ghahramani

Bayesian Sparse Sampling for On-Line Reward Optimization Tao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans

Beyond the Point Cloud: From Transductive to Semi-Supervised Learning Vikas Sindhwani, Partha Niyogi, Mikhail Belkin

Bounded Real-Time Dynamic Programming: RTDP with Monotone Upper Bounds and Performance Guarantees H. Brendan McMahan, Maxim Likhachev, Geoffrey J. Gordon

Building Sparse Large Margin Classifiers Mingrui Wu, Bernhard Schölkopf, Gökhan H. Bakir

Closed-Form Dual Perturb and Combine for Tree-Based Models Pierre Geurts, Louis Wehenkel

Clustering Through Ranking on Manifolds Markus Breitenbach, Gregory Z. Grudic

Coarticulation: An Approach for Generating Concurrent Plans in Markov Decision Processes Khashayar Rohanimanesh, Sridhar Mahadevan

Combining Model-Based and Instance-Based Learning for First Order Regression Kurt Driessens, Saso Dzeroski

Compact Approximations to Bayesian Predictive Distributions Edward Lloyd Snelson, Zoubin Ghahramani

Comparing Clusterings: An Axiomatic View Marina Meila

Computational Aspects of Bayesian Partition Models Mikko Koivisto, Kismat Sood

Core Vector Regression for Very Large Regression Problems Ivor W. Tsang, James T. Kwok, Kimo T. Lai

Dirichlet Enhanced Relational Learning Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, Hans-Peter Kriegel

Discriminative Versus Generative Parameter and Structure Learning of Bayesian Network Classifiers Franz Pernkopf, Jeff A. Bilmes

Dynamic Preferences in Multi-Criteria Reinforcement Learning Sriraam Natarajan, Prasad Tadepalli

Efficient Discriminative Learning of Bayesian Network Classifier via Boosted Augmented Naive Bayes Yushi Jing, Vladimir Pavlovic, James M. Rehg

Ensembles of Biased Classifiers Rinat Khoussainov, Andreas Heß, Nicholas Kushmerick

Error Bounds for Correlation Clustering Thorsten Joachims, John E. Hopcroft

Error Limiting Reductions Between Classification Tasks Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford, Bianca Zadrozny

Estimating and Computing Density Based Distance Metrics Sajama, Alon Orlitsky

Evaluating Machine Learning for Information Extraction Neil Ireson, Fabio Ciravegna, Mary Elaine Califf, Dayne Freitag, Nicholas Kushmerick, Alberto Lavelli

Expectation Maximization Algorithms for Conditional Likelihoods Jarkko Salojärvi, Kai Puolamäki, Samuel Kaski

Experimental Comparison Between Bagging and Monte Carlo Ensemble Classification Roberto Esposito, Lorenza Saitta

Explanation-Augmented SVM: An Approach to Incorporating Domain Knowledge into SVM Learning Qiang Sun, Gerald DeJong

Exploiting Syntactic, Semantic and Lexical Regularities in Language Modeling via Directed Markov Random Fields Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng

Exploration and Apprenticeship Learning in Reinforcement Learning Pieter Abbeel, Andrew Y. Ng

Fast Condensed Nearest Neighbor Rule Fabrizio Angiulli

Fast Inference and Learning in Large-State-Space HMMs Sajid M. Siddiqi, Andrew W. Moore

Fast Maximum Margin Matrix Factorization for Collaborative Prediction Jason D. M. Rennie, Nathan Srebro

Finite Time Bounds for Sampling Based Fitted Value Iteration Csaba Szepesvári, Rémi Munos

Generalized LARS as an Effective Feature Selection Tool for Text Classification with SVMs S. Sathiya Keerthi

Generalized Skewing for Functions with Continuous and Nominal Attributes Soumya Ray, David Page

Harmonic Mixtures: Combining Mixture Models and Graph-Based Methods for Inductive and Scalable Semi-Supervised Learning Xiaojin Zhu, John D. Lafferty

Healing the Relevance Vector Machine Through Augmentation Carl Edward Rasmussen, Joaquin Quiñonero Candela

Hedged Learning: Regret-Minimization with Learning Experts Yu-Han Chang, Leslie Pack Kaelbling

Heteroscedastic Gaussian Process Regression Quoc V. Le, Alexander J. Smola, Stéphane Canu

Hierarchic Bayesian Models for Kernel Learning Mark A. Girolami, Simon Rogers

Hierarchical Dirichlet Model for Document Classification Sriharsha Veeramachaneni, Diego Sona, Paolo Avesani

High Speed Obstacle Avoidance Using Monocular Vision and Reinforcement Learning Jeff Michels, Ashutosh Saxena, Andrew Y. Ng

Identifying Useful Subgoals in Reinforcement Learning by Local Graph Partitioning Özgür Simsek, Alicia P. Wolfe, Andrew G. Barto

Implicit Surface Modelling as an Eigenvalue Problem Christian Walder, Olivier Chapelle, Bernhard Schölkopf

Incomplete-Data Classification Using Logistic Regression David Williams, Xuejun Liao, Ya Xue, Lawrence Carin

Independent Subspace Analysis Using Geodesic Spanning Trees Barnabás Póczos, András Lörincz

Integer Linear Programming Inference for Conditional Random Fields Dan Roth, Wen-tau Yih

Interactive Learning of Mappings from Visual Percepts to Actions Sébastien Jodogne, Justus H. Piater

Intrinsic Dimensionality Estimation of Submanifolds in Rd Matthias Hein, Jean-Yves Audibert

Large Margin Non-Linear Embedding Alexander Zien, Joaquin Quiñonero Candela

Large Scale Genomic Sequence SVM Classifiers Sören Sonnenburg, Gunnar Rätsch, Bernhard Schölkopf

Learn to Weight Terms in Information Retrieval Using Category Information Rong Jin, Joyce Y. Chai, Luo Si

Learning Approximate Preconditions for Methods in Hierarchical Plans Okhtay Ilghami, Héctor Muñoz-Avila, Dana S. Nau, David W. Aha

Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction Hal Daumé Iii, Daniel Marcu

Learning Class-Discriminative Dynamic Bayesian Networks John Burge, Terran Lane

Learning Discontinuities with Products-of-Sigmoids for Switching Between Local Models Marc Toussaint, Sethu Vijayakumar

Learning First-Order Probabilistic Models with Combining Rules Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo C. Restificar

Learning from Labeled and Unlabeled Data on a Directed Graph Dengyong Zhou, Jiayuan Huang, Bernhard Schölkopf

Learning Gaussian Processes from Multiple Tasks Kai Yu, Volker Tresp, Anton Schwaighofer

Learning Hierarchical Multi-Category Text Classification Models Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor

Learning Predictive Representations from a History Eric Wiewiora

Learning Predictive State Representations in Dynamical Systems Without Reset Britton Wolfe, Michael R. James, Satinder Singh

Learning Strategies for Story Comprehension: A Reinforcement Learning Approach Eugene Grois, David C. Wilkins

Learning Structured Prediction Models: A Large Margin Approach Benjamin Taskar, Vassil Chatalbashev, Daphne Koller, Carlos Guestrin

Learning the Structure of Markov Logic Networks Stanley Kok, Pedro M. Domingos

Learning to Compete, Compromise, and Cooperate in Repeated General-Sum Games Jacob W. Crandall, Michael A. Goodrich

Learning to Rank Using Gradient Descent Christopher J. C. Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, Gregory N. Hullender

Linear Asymmetric Classifier for Cascade Detectors Jianxin Wu, Matthew D. Mullin, James M. Rehg

Logistic Regression with an Auxiliary Data Source Xuejun Liao, Ya Xue, Lawrence Carin

Modeling Word Burstiness Using the Dirichlet Distribution Rasmus Elsborg Madsen, David Kauchak, Charles Elkan

Multi-Class Protein Fold Recognition Using Adaptive Codes Eugene Ie, Jason Weston, William Stafford Noble, Christina S. Leslie

Multi-Instance Tree Learning Hendrik Blockeel, David Page, Ashwin Srinivasan

Multi-Way Distributional Clustering via Pairwise Interactions Ron Bekkerman, Ran El-Yaniv, Andrew McCallum

Multimodal Oriented Discriminant Analysis Fernando De la Torre, Takeo Kanade

Naive Bayes Models for Probability Estimation Daniel Lowd, Pedro M. Domingos

Near-Optimal Sensor Placements in Gaussian Processes Carlos Guestrin, Andreas Krause, Ajit Paul Singh

New Approaches to Support Vector Ordinal Regression Wei Chu, S. Sathiya Keerthi

New D-Separation Identification Results for Learning Continuous Latent Variable Models Ricardo Bezerra de Andrade e Silva, Richard Scheines

New Kernels for Protein Structural Motif Discovery and Function Classification Chang Wang, Stephen D. Scott

Non-Negative Tensor Factorization with Applications to Statistics and Computer Vision Amnon Shashua, Tamir Hazan

Object Correspondence as a Machine Learning Problem Bernhard Schölkopf, Florian Steinke, Volker Blanz

Online Feature Selection for Pixel Classification Karen A. Glocer, Damian Eads, James Theiler

Online Learning over Graphs Mark Herbster, Massimiliano Pontil, Lisa Wainer

Optimal Assignment Kernels for Attributed Molecular Graphs Holger Fröhlich, Jörg K. Wegner, Florian Sieker, Andreas Zell

Optimizing Abstaining Classifiers Using ROC Analysis Tadeusz Pietraszek

PAC-Bayes Risk Bounds for Sample-Compressed Gibbs Classifiers François Laviolette, Mario Marchand

Predicting Good Probabilities with Supervised Learning Alexandru Niculescu-Mizil, Rich Caruana

Predicting Probability Distributions for Surf Height Using an Ensemble of Mixture Density Networks Michael Carney, Padraig Cunningham, Jim Dowling, Ciaran Lee

Predicting Protein Folds with Structural Repeats Using a Chain Graph Model Yan Liu, Eric P. Xing, Jaime G. Carbonell

Predicting Relative Performance of Classifiers from Samples Rui Leite, Pavel Brazdil

Predictive Low-Rank Decomposition for Kernel Methods Francis R. Bach, Michael I. Jordan

Preference Learning with Gaussian Processes Wei Chu, Zoubin Ghahramani

Propagating Distributions on a Hypergraph by Dual Information Regularization Koji Tsuda

Proto-Value Functions: Developmental Reinforcement Learning Sridhar Mahadevan

Q-Learning of Sequential Attention for Visual Object Recognition from Informative Local Descriptors Lucas Paletta, Gerald Fritz, Christin Seifert

Recognition and Reproduction of Gestures Using a Probabilistic Framework Combining PCA, ICA and HMM Sylvain Calinon, Aude Billard

Recycling Data for Multi-Agent Learning Santiago Ontañón, Enric Plaza

Reducing Overfitting in Process Model Induction Will Bridewell, Narges Bani Asadi, Pat Langley, Ljupco Todorovski

Reinforcement Learning with Gaussian Processes Yaakov Engel, Shie Mannor, Ron Meir

Relating Reinforcement Learning Performance to Classification Performance John Langford, Bianca Zadrozny

Robust One-Class Clustering Using Hybrid Global and Local Search Gunjan Gupta, Joydeep Ghosh

ROC Confidence Bands: An Empirical Evaluation Sofus A. Macskassy, Foster J. Provost, Saharon Rosset

Semi-Supervised Graph Clustering: A Kernel Approach Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney

Statistical and Computational Analysis of Locality Preserving Projection Xiaofei He, Deng Cai, Wanli Min

Supervised Clustering with Support Vector Machines Thomas Finley, Thorsten Joachims

Supervised Dimensionality Reduction Using Mixture Models Sajama, Alon Orlitsky

Supervised Versus Multiple Instance Learning: An Empirical Comparison Soumya Ray, Mark Craven

TD(lambda) Networks: Temporal-Difference Networks with Eligibility Traces Brian Tanner, Richard S. Sutton

Tempering for Bayesian C&RT Nicos Angelopoulos, James Cussens

The Cross Entropy Method for Classification Shie Mannor, Dori Peleg, Reuven Y. Rubinstein

Unifying the Error-Correcting and Output-Code AdaBoost Within the Margin Framework Yijun Sun, Sinisa Todorovic, Jian Li, Dapeng Wu

Unsupervised Evidence Integration Philip M. Long, Vinay Varadan, Sarah Gilman, Mark Treshock, Rocco A. Servedio

Using Additive Expert Ensembles to Cope with Concept Drift Jeremy Z. Kolter, Marcus A. Maloof

Variational Bayesian Image Modelling Li Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang

Weighted Decomposition Kernels Sauro Menchetti, Fabrizio Costa, Paolo Frasconi

Why Skewing Works: Learning Difficult Boolean Functions with Greedy Tree Learners Bernard Rosell, Lisa Hellerstein, Soumya Ray, David Page