ICML 2006 Proceedings

Please refer to papers in this volume as:
William Cohen, Andrew Moore (Eds.) Proceedings of the 23nd International Machine Learning Conference, Omni Press, 2006
Order in Contents Paper Authors

001

Using Inaccurate Models in Reinforcement Learning

Pieter Abbeel, Morgan Quigley, Andrew Y. Ng

002

Algorithms for Portfolio Management Based on the Newton Method

Amit Agarwal, Elad Hazan, Satyen Kale, Robert E. Schapire

003

Higher Order Learning with Graphs

Sameer Agarwal, Kristin Branson, Serge Belongie

004

Ranking on Graph Data

Shivani Agarwal

005

Robust Probabilistic Projections

Cédric Archambeau, Nicolas Delannay, Michel Verleysen

006

A DC-Programming Algorithm for Kernel Selection

Andreas Argyriou, Raphael Hauser, Charles A. Micchelli, Massimiliano Pontil

007

Relational Temporal Difference Learning

Nima Asgharbeygi, David Stracuzzi, Pat Langley

008

A New Approach to Data Driven Clustering

Arik Azran, Zoubin Ghahramani

009

Agnostic Active Learning

Maria-Florina Balcan, Alina Beygelzimer, John Langford

010

On a Theory of Learning with Similarity Functions

Maria-Florina Balcan, Avrim Blum

011

On Bayesian Bounds

Arindam Banerjee

012

Convex Optimization Techniques for Fitting Sparse Gaussian Graphical Models

Onureena Banerjee, Laurent El Ghaoui, Alexandre d'Aspremont, Georges Natsoulis

013

Cover Trees for Nearest Neighbor

Alina Beygelzimer, Sham Kakade, John Langford

014

Graph Model Selection using Maximum Likelihood

Ivona Bezáková, Adam Kalai, Rahul Santhanam

015

Dynamic Topic Models

David M. Blei, John D. Lafferty

016

Predictive Search Distributions

Edwin V. Bonilla, Christopher K.I. Williams, Felix V. Agakov, John Cavazos, John Thomson, Michael F.P. O'Boyle

017

Learning Predictive State Representations using Non-Blind Policies

Michael Bowling, Peter McCracken, Michael James, James Neufeld, Dana Wilkinson

018

Efficient Co-Regularized Least Squares Regression

Ulf Brefeld, Thomas Gärtner, Tobias Scheffer, Stefan Wrobel

019

Semi-Supervised Learning for Structured Output Variables

Ulf Brefeld, Tobias Scheffer

020

Fast Nonparametric Clustering with Gaussian Blurring Mean-Shift

Miguel Á. Carreira-Perpiñán

021

An Empirical Comparison of Supervised Learning Algorithms

Rich Caruana, Alexandru Niculescu-Mizil

022

Robust Euclidean Embedding

Lawrence Cayton, Sanjoy Dasgupta

023

Hierarchical Classification: Combining Bayes with SVM

Nicolò Cesa-Bianchi, Claudio Gentile, Luca Zaniboni

024

A Continuation Method for Semi-Supervised SVMs

Olivier Chapelle, Mingmin Chi, Alexander Zien

025

A Regularization Framework for Multiple-Instance Learning

Pak-Ming Cheung, James T. Kwok

026

Trading Convexity for Scalability

Ronan Collobert, Fabian Sinz, Jason Weston, Léon Bottou

027

Learning Algorithms for Online Principal-Agent Problems (and Selling Goods Online)

Vincent Conitzer, Nikesh Garera

028

Dealing with Non-Stationary Environments using Context Detection

Bruno C. da Silva, Eduardo W. Basso, Ana L. C. Bazzan, Paulo M. Engel

029

Locally Adaptive Classification Piloted by Uncertainty

Juan Dai, Shuicheng Yan, Xiaoou Tang, James T. Kwok

030

The Relationship Between Precision-Recall and ROC Curves

Jesse Davis, Mark Goadrich

031

Discriminative Cluster Analysis

Fernando De la Torre, Takeo Kanade

032

Collaborative Prediction using Ensembles of Maximum Margin Matrix Factorizations

Dennis DeCoste

033

Learning the Structure of Factored Markov Decision Processes in Reinforcement Learning Problems

Thomas Degris, Olivier Sigaud, Pierre-Henri Wuillemin

034

Efficient Learning of Naive Bayes Classifiers under Class-Conditional Classification Noise

Francois Denis, Christophe Nicolas Magnan, Liva Ralaivola

035

Learning User Preferences for Sets of Objects

Marie desJardins, Eric Eaton, Kiri L. Wagstaff

036

R1-PCA: Rotational Invariant L1-norm Principal Component Analysis for Robust Subspace Factorization

Chris Ding, Ding Zhou, Xiaofeng He, Hongyuan Zha

037

Clustering Documents with an Exponential-Family Approximation of the Dirichlet Compound Multinomial Distribution

Charles Elkan

038

A Graphical Model for Predicting Protein Molecular Function

Barbara Engelhardt, Michael Jordan, Steven Brenner

039

Qualitative Reinforcement Learning

Arkady Epshteyn, Gerald DeJong

040

Online Multiclass Learning by Interclass Hypothesis Sharing

Michael Fink, Shai Shalev-Shwartz, Yoram Singer, Shimon Ullman

041

Regression with the Optimised Combination Technique

Jochen Garcke

042

A Note on Mixtures of Experts for Multiclass Responses: Approximation Rate and Consistent Bayesian Inference

Yang Ge, Wenxin Jiang

043

The Rate Adapting Poisson Model for Information Retrieval and Object Recognition

Peter V. Gehler, Alex D. Holub, Max Welling

044

Kernelizing the Output of Tree-Based Methods

Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc

045

Nightmare at Test Time: Robust Learning by Feature Deletion

Amir Globerson, Sam Roweis

046

A Choice Model with Infinitely Many Latent Features

Dilan Görür, Frank Jäkel, Carl Edward Rasmussen

047

Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks

Alex Graves, Santiago Fernández, Faustino Gomez, Juergen Schmidhuber

048

Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering

Derek Greene, Pádraig Cunningham

049

Fast Transpose Methods for Kernel Learning on Sparse Data

Patrick Haffner

050

An Analysis of Graph Cut Size for Transductive Learning

Steve Hanneke

051

Learning a Kernel Function for Classification with Small Training Samples

Tomer Hertz, Aharon Bar Hillel, Daphna Weinshall

052

Looping Suffix Tree-Based Inference of Partially Observable Hidden State

Michael Holmes, Charles Lee Isbell, Jr.

053

Batch Mode Active Learning and Its Application to Medical Image Classification

Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu

054

Ranking Individuals by Group Comparisons

Tzu-Kuo Huang, Chih-Jen Lin, Ruby C. Weng

055

Hidden Process Models

Rebecca A. Hutchinson, Tom M. Mitchell, Indrayana Rustandi

056

Estimating Relatedness via Data Compression

Brendan Juba

057

Automatic Basis Function Construction for Approximate Dynamic Programming and Reinforcement Learning

Philipp W. Keller, Shie Mannor, Doina Precup

058

Personalized Handwriting Recognition via Biased Regularization

Wolf Kienzle, Kumar Chellapilla

059

Optimal Kernel Selection in Kernel Fisher Discriminant Analysis

Seung-Jean Kim, Alessandro Magnani, Stephen Boyd

060

Pareto Optimal Linear Classification

Seung-Jean Kim, Alessandro Magnani, Sikandar Samar, Stephen Boyd, Johan Lim

061

Fast Particle Smoothing: If I Had a Million Particles

Mike Klaas, Mark Briers, Nando de Freitas, Arnaud Doucet, Simon Maskell, Dustin Lang

062

Autonomous Shaping: Knowledge Transfer in Reinforcement Learning

George Konidaris, Andrew Barto

063

Data Association for Topic Intensity Tracking

Andreas Krause, Jure Leskovec, Carlos Guestrin

064

Learning Low-Rank Kernel Matrices

Brian Kulis, Mátyás Sustik, Inderjit Dhillon

065

Local Distance Preservation in the GP-LVM through Back Constraints

Neil D. Lawrence, Joaquin Quiñonero-Candela

066

Simpler Knowledge-based Support Vector Machines

Quoc V. Le, Alex J. Smola, Thomas Gärtner

067

Using Query-Specific Variance Estimates to Combine Bayesian Classifiers

Chi-Hoon Lee, Russ Greiner, Shaojun Wang

068

A Probabilistic Model for Text Kernels

Alain Lehmann, John Shawe-Taylor

069

Efficient MAP Approximation for Dense Energy Functions

Marius Leordeanu, Martial Hebert

070

Nonstationary Kernel Combination

Darrin P. Lewis, Tony Jebara, William Stafford Noble

071

Region-Based Value Iteration for Partially Observable Markov Decision Processes

Hui Li, Xuejun Liao, Lawrence Carin

072

Multiclass Boosting with Repartitioning

Ling Li

073

Pachinko Allocation: DAG-Structured Mixture Models of Topic Correlations

Wei Li, Andrew McCallum

074

Spectral Clustering for Multi-type Relational Data

Bo Long, Mark Zhang, Xiaoyun Wu, Philip S. Yu

075

Combined Central and Subspace Clustering for Computer Vision Applications

Le Lu, René Vidal

076

Fast Direct Policy Evaluation using Multiscale Analysis of Markov Diffusion Processes

Mauro Maggioni, Sridhar Mahadevan

077

Pruning in Ordered Bagging Ensembles

Gonzalo Martínez-Muñoz, Alberto Suárez

078

Learning High-Order MRF Priors of Color Images

Julian J. McAuley, Tibério S. Caetano, Alex J. Smola, Matthias O. Franz

079

The Uniqueness of a Good Optimum for K-means

Marina Meila

080

Kernel Information Embeddings

Roland Memisevic

081

Generalized Spectral Bounds for Sparse LDA

Baback Moghaddam, Yair Weiss, Shai Avidan

082

Learning to Impersonate

Moni Naor, Guy N. Rothblum

083

Online Decoding of Markov Models under Latency Constraints

Mukund Narasimhan, Paul Viola, Michael Shilman

084

Learning Hierarchical Task Networks by Observation

Negin Nejati, Pat Langley, Tolga Konik

085

Reinforcement Learning for Optimized Trade Execution

Yuriy Nevmyvaka, Yi Feng, Michael Kearns

086

Concept Boundary Detection for Speeding up SVMs

Navneet Panda, Edward Y. Chang, Gang Wu

087

The Support Vector Decomposition Machine

Francisco Pereira, Geoffrey Gordon

088

An Analytic Solution to Discrete Bayesian Reinforcement Learning

Pascal Poupart, Nikos Vlassis, Jesse Hoey, Kevin Regan

089

MISSL: Multiple-Instance Semi-Supervised Learning

Rouhollah Rahmani, Sally A. Goldman

090

Constructing Informative Priors using Transfer Learning

Rajat Raina, Andrew Y. Ng, Daphne Koller

091

CN=CPCN

Liva Ralaivola, François Denis, Christophe N. Magnan

092

Maximum Margin Planning

Nathan D. Ratliff, J. Andrew Bagnell, Martin A. Zinkevich

093

Quadratic Programming Relaxations for Metric Labeling and Markov Random Field MAP Estimation

Pradeep Ravikumar, John Lafferty

094

Categorization in Multiple Category Systems

Jean-Michel Renders, Eric Gaussier, Cyril Goutte, Francois Pacull, Gabriela Csurka

095

How Boosting the Margin Can Also Boost Classifier Complexity

Lev Reyzin, Robert E. Schapire

096

Combining Discriminative Features to Infer Complex Trajectories

David A. Ross, Simon Osindero, Richard S. Zemel

097

Sequential Update of ADtrees

Josep Roure, Andrew W. Moore

098

Predictive Linear-Gaussian Models of Controlled Stochastic Dynamical Systems

Matthew Rudary, Satinder Singh

099

A Statistical Approach to Rule Learning

Ulrich Rückert, Stefan Kramer

100

Efficient Inference on Sequence Segmentation Models

Sunita Sarawagi

101

Cost-Sensitive Learning with Conditional Markov Networks

Prithviraj Sen, Lise Getoor

102

Feature Value Acquisition in Testing: A Sequential Batch Test Algorithm

Victor S. Sheng, Charles X. Ling

103

Permutation Invariant SVMs

Pannagadatta K. Shivaswamy, Tony Jebara

104

Bayesian Learning of Measurement and Structural Models

Ricardo Silva, Richard Scheines

105

An Intrinsic Reward Mechanism for Efficient Exploration

Özgür Simsek, Andrew G. Barto

106

Deterministic Annealing for Semi-supervised Kernel Machines

Vikas Sindhwani, Sathiya Keerthi, Olivier Chapelle

107

Feature Subset Selection Bias for Classification Learning

Surendra K. Singhi, Huan Liu

108

Classifying EEG for Brain-Computer Interfaces: Learning Optimal Filters for Dynamical System Features

Le Song, Julien Epps

109

An Investigation of Computational and Informational Limits in Gaussian Mixture Clustering

Nathan Srebro, Gregory Shakhnarovich, Sam Roweis

110

Bayesian Pattern Ranking for Move Prediction in the Game of Go

David Stern, Ralf Herbrich, Thore Graepel

111

PAC Model-free Reinforcement Learning

Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman

112

Experience-Efficient Learning in Associative Bandit Problems

Alexander L. Strehl, Chris Mesterharm, Michael L. Littman, Haym Hirsh

113

Full Bayesian Network Classifiers

Jiang Su, Harry Zhang

114

Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction

Masashi Sugiyama

115

Iterative RELIEF for Feature Weighting

Yijun Sun, Jian Li

116

Multiclass Reduced-Set Support Vector Machines

Benyang Tang, Dominic Mazzoni

117

Fast and Space Efficient String Kernels using Suffix Arrays

Choon Hui Teo, S.V.N. Vishwanathan

118

Bayesian Regression with Input Noise for High Dimensional Data

Jo-Anne Ting, Aaron D'Souza, Stefan Schaal

119

Probabilistic Inference for Solving Discrete and Continuous State Markov Decision Processes

Marc Toussaint, Amos Storkey

120

Clustering Graphs by Weighted Substructure Mining

Koji Tsuda, Taku Kudo

121

Active Sampling for Detecting Irrelevant Features

Sriharsha Veeramachaneni, Emanuele Olivetti, Paolo Avesani

122

Accelerated Training of Conditional Random Fields with Stochastic Gradient Methods

S.V.N. Vishwanathan, Nicol N. Schraudolph, Mark W. Schmidt, Kevin P. Murphy

123

Topic Modeling: Beyond Bag-of-Words

Hanna M. Wallach

124

Label Propagation through Linear Neighborhoods

Fei Wang, Changshui Zhang

125

Two-Dimensional Solution Path for Support Vector Regression

Gang Wang, Dit-Yan Yeung, Frederick H. Lochovsky

126

Totally Corrective Boosting Algorithms that Maximize the Margin

Manfred K. Warmuth, Jun Liao, Gunnar Rätsch

127

Inference with the Universum

Jason Weston, Ronan Collobert, Fabian Sinz, Léon Bottou, Vladimir Vapnik

128

Kernel Predictive Linear Gaussian Models for Nonlinear Stochastic Dynamical Systems

David Wingate, Satinder Singh

129

Predictive State Representations with Options

Britton Wolfe, Satinder Singh

130

Fast Time Series Classification using Numerosity Reduction

Xiaopeng Xi, Eamonn Keogh, Christian Shelton, Li Wei, Chotirat Ann Ratanamahatana

131

A Duality View of Spectral Methods for Dimensionality Reduction

Lin Xiao, Jun Sun, Stephen Boyd

132

Bayesian Multi-Population Haplotype Inference via a Hierarchical Dirichlet Process Mixture

Eric P. Xing, Kyung-Ah Sohn, Michael I. Jordan, Yee-Whye Teh

133

Discriminative Unsupervised Learning of Structured Predictors

Linli Xu, Dana Wilkinson, Finnegan Southey, Dale Schuurmans

134

Semi-Supervised Nonlinear Dimensionality Reduction

Xin Yang, Haoying Fu, Hongyuan Zha, Jesse Barlow

135

Null Space versus Orthogonal Linear Discriminant Analysis

Jieping Ye, Tao Xiong

136

Active Learning via Transductive Experimental Design

Kai Yu, Jinbo Bi, Volker Tresp

137

Collaborative Ordinal Regression

Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Kriegel

138

Block-Quantized Kernel Matrix for Fast Spectral Embedding

Kai Zhang, James Kwok

139

Statistical Debugging: Simultaneous Identification of Multiple Bugs

Alice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, Alex Aiken

140

Efficient Lazy Elimination for Averaged One-Dependence Estimators

Fei Zheng, Geoffrey I. Webb