ICML 2007

150 papers

A Bound on the Label Complexity of Agnostic Active Learning Steve Hanneke

A Dependence Maximization View of Clustering Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt

A Fast Linear Separability Test by Projection of Positive Points on Subspaces A. P. Yogananda, M. Narasimha Murty, Lakshmi Gopal

A Kernel Path Algorithm for Support Vector Machines Gang Wang, Dit-Yan Yeung, Frederick H. Lochovsky

A Kernel-Based Causal Learning Algorithm Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf, Kenji Fukumizu

A Novel Orthogonal NMF-Based Belief Compression for POMDPs Xin Li, William Kwok-Wai Cheung, Jiming Liu, Zhili Wu

A Permutation-Augmented Sampler for DP Mixture Models Percy Liang, Michael I. Jordan, Benjamin Taskar

A Recursive Method for Discriminative Mixture Learning Minyoung Kim, Vladimir Pavlovic

A Transductive Framework of Distance Metric Learning by Spectral Dimensionality Reduction Fuxin Li, Jian Yang, Jue Wang

Adaptive Dimension Reduction Using Discriminant Analysis and K-Means Clustering Chris H. Q. Ding, Tao Li

Adaptive Mesh Compression in 3D Computer Graphics Using Multiscale Manifold Learning Sridhar Mahadevan

An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation Hugo Larochelle, Dumitru Erhan, Aaron C. Courville, James Bergstra, Yoshua Bengio

An Integrated Approach to Feature Invention and Model Construction for Drug Activity Prediction Jesse Davis, Vítor Santos Costa, Soumya Ray, David Page

Analyzing Feature Generation for Value-Function Approximation Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman

Approximate Maximum Margin Algorithms with Rules Controlled by the Number of Mistakes Petroula Tsampouka, John Shawe-Taylor

Asymmetric Boosting Hamed Masnadi-Shirazi, Nuno Vasconcelos

Asymptotic Bayesian Generalization Error When Training and Test Distributions Are Different Keisuke Yamazaki, Motoaki Kawanabe, Sumio Watanabe, Masashi Sugiyama, Klaus-Robert Müller

Automatic Shaping and Decomposition of Reward Functions Bhaskara Marthi

Bayesian Actor-Critic Algorithms Mohammad Ghavamzadeh, Yaakov Engel

Bayesian Compressive Sensing and Projection Optimization Shihao Ji, Lawrence Carin

Beamforming Using the Relevance Vector Machine David P. Wipf, Srikantan S. Nagarajan

Best of Both: A Hybridized Centroid-Medoid Clustering Heuristic Nizar Grira, Michael E. Houle

Boosting for Transfer Learning Wenyuan Dai, Qiang Yang, Gui-Rong Xue, Yong Yu

Bottom-up Learning of Markov Logic Network Structure Lilyana Mihalkova, Raymond J. Mooney

CarpeDiem: An Algorithm for the Fast Evaluation of SSL Classifiers Roberto Esposito, Daniele Paolo Radicioni

Classifying Matrices with a Spectral Regularization Ryota Tomioka, Kazuyuki Aihara

Cluster Analysis of Heterogeneous Rank Data Ludwig M. Busse, Peter Orbanz, Joachim M. Buhmann

Combining Online and Offline Knowledge in UCT Sylvain Gelly, David Silver

Comparisons of Sequence Labeling Algorithms and Extensions Nam Nguyen, Yunsong Guo

Conditional Random Fields for Multi-Agent Reinforcement Learning Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanathan

Constructing Basis Functions from Directed Graphs for Value Function Approximation Jeffrey Johns, Sridhar Mahadevan

Cross-Domain Transfer for Reinforcement Learning Matthew E. Taylor, Peter Stone

Dimensionality Reduction and Generalization Sofia Mosci, Lorenzo Rosasco, Alessandro Verri

Direct Convex Relaxations of Sparse SVM Antoni B. Chan, Nuno Vasconcelos, Gert R. G. Lanckriet

Dirichlet Aggregation: Unsupervised Learning Towards an Optimal Metric for Proportional Data Hua-Yan Wang, Hongbin Zha, Hong Qin

Discriminant Analysis in Correlation Similarity Measure Space Yong Ma, Shihong Lao, Erina Takikawa, Masato Kawade

Discriminant Kernel and Regularization Parameter Learning via Semidefinite Programming Jieping Ye, Jianhui Chen, Shuiwang Ji

Discriminative Gaussian Process Latent Variable Model for Classification Raquel Urtasun, Trevor Darrell

Discriminative Learning for Differing Training and Test Distributions Steffen Bickel, Michael Brückner, Tobias Scheffer

Dynamic Hierarchical Markov Random Fields and Their Application to Web Data Extraction Jun Zhu, Zaiqing Nie, Bo Zhang, Ji-Rong Wen

Efficient Inference with Cardinality-Based Clique Potentials Rahul Gupta, Ajit A. Diwan, Sunita Sarawagi

Efficiently Computing Minimax Expected-Size Confidence Regions Brent Bryan, H. Brendan McMahan, Chad M. Schafer, Jeff G. Schneider

Entire Regularization Paths for Graph Data Koji Tsuda

Experimental Perspectives on Learning from Imbalanced Data Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano

Exponentiated Gradient Algorithms for Log-Linear Structured Prediction Amir Globerson, Terry Koo, Xavier Carreras, Michael Collins

Fast and Effective Kernels for Relational Learning from Texts Alessandro Moschitti, Fabio Massimo Zanzotto

Feature Selection in a Kernel Space Bin Cao, Dou Shen, Jian-Tao Sun, Qiang Yang, Zheng Chen

Focused Crawling with Scalable Ordinal Regression Solvers Rashmin Babaria, J. Saketha Nath, S. Krishnan, K. R. Sivaramakrishnan, Chiranjib Bhattacharyya, M. Narasimha Murty

Full Regularization Path for Sparse Principal Component Analysis Alexandre d'Aspremont, Francis R. Bach, Laurent El Ghaoui

Gradient Boosting for Kernelized Output Spaces Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc

Graph Clustering with Network Structure Indices Matthew J. Rattigan, Marc E. Maier, David D. Jensen

Hierarchical Gaussian Process Latent Variable Models Neil D. Lawrence, Andrew J. Moore

Hierarchical Maximum Entropy Density Estimation Miroslav Dudík, David M. Blei, Robert E. Schapire

Hybrid Huberized Support Vector Machines for Microarray Classification Li Wang, Ji Zhu, Hui Zou

Incremental Bayesian Networks for Structure Prediction Ivan Titov, James Henderson

Infinite Mixtures of Trees Sergey Kirshner, Padhraic Smyth

Information-Theoretic Metric Learning Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon

Intractability and Clustering with Constraints Ian Davidson, S. S. Ravi

Kernel Selection Forl Semi-Supervised Kernel Machines Guang Dai, Dit-Yan Yeung

Kernelizing PLS, Degrees of Freedom, and Efficient Model Selection Nicole Krämer, Mikio L. Braun

Large-Scale RLSC Learning Without Agony Wenye Li, Kin-Hong Lee, Kwong-Sak Leung

Learning a Meta-Level Prior for Feature Relevance from Multiple Related Tasks Su-In Lee, Vassil Chatalbashev, David Vickrey, Daphne Koller

Learning Distance Function by Coding Similarity Aharon Bar-Hillel, Daphna Weinshall

Learning for Efficient Retrieval of Structured Data with Noisy Queries Charles Parker, Alan Fern, Prasad Tadepalli

Learning from Interpretations: A Rooted Kernel for Ordered Hypergraphs Gabriel Wachman, Roni Khardon

Learning Nonparametric Kernel Matrices from Pairwise Constraints Steven C. H. Hoi, Rong Jin, Michael R. Lyu

Learning Random Walks to Rank Nodes in Graphs Alekh Agarwal, Soumen Chakrabarti

Learning State-Action Basis Functions for Hierarchical MDPs Sarah Osentoski, Sridhar Mahadevan

Learning to Combine Distances for Complex Representations Adam Woznica, Alexandros Kalousis, Melanie Hilario

Learning to Compress Images and Videos Li Cheng, S. V. N. Vishwanathan

Learning to Rank: From Pairwise Approach to Listwise Approach Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Hang Li

Learning to Solve Game Trees David H. Stern, Ralf Herbrich, Thore Graepel

Least Squares Linear Discriminant Analysis Jieping Ye

Linear and Nonlinear Generative Probabilistic Class Models for Shape Contours Graham McNeill, Sethu Vijayakumar

Local Dependent Components Arto Klami, Samuel Kaski

Local Learning Projections Mingrui Wu, Kai Yu, Shipeng Yu, Bernhard Schölkopf

Local Similarity Discriminant Analysis Luca Cazzanti, Maya R. Gupta

Magnitude-Preserving Ranking Algorithms Corinna Cortes, Mehryar Mohri, Ashish Rastogi

Manifold-Adaptive Dimension Estimation Amir Massoud Farahmand, Csaba Szepesvári, Jean-Yves Audibert

mAP Building Without Localization by Dimensionality Reduction Techniques Takehisa Yairi

Maximum Margin Clustering Made Practical Kai Zhang, Ivor W. Tsang, James T. Kwok

Minimum Reference Set Based Feature Selection for Small Sample Classifications Xue-wen Chen, Jong Cheol Jeong

Mixtures of Hierarchical Topics with Pachinko Allocation David M. Mimno, Wei Li, Andrew McCallum

Modeling Changing Dependency Structure in Multivariate Time Series Xiang Xuan, Kevin P. Murphy

More Efficiency in Multiple Kernel Learning Alain Rakotomamonjy, Francis R. Bach, Stéphane Canu, Yves Grandvalet

Most Likely Heteroscedastic Gaussian Process Regression Kristian Kersting, Christian Plagemann, Patrick Pfaff, Wolfram Burgard

Multi-Armed Bandit Problems with Dependent Arms Sandeep Pandey, Deepayan Chakrabarti, Deepak Agarwal

Multi-Task Learning for Sequential Data via iHMMs and the Nested Dirichlet Process Kai Ni, Lawrence Carin, David B. Dunson

Multi-Task Reinforcement Learning: A Hierarchical Bayesian Approach Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepalli

Multiclass Core Vector Machine S. Asharaf, M. Narasimha Murty, Shirish K. Shevade

Multiclass Multiple Kernel Learning Alexander Zien, Cheng Soon Ong

Multifactor Gaussian Process Models for Style-Content Separation Jack M. Wang, David J. Fleet, Aaron Hertzmann

Multiple Instance Learning for Sparse Positive Bags Razvan C. Bunescu, Raymond J. Mooney

Neighbor Search with Global Geometry: A Minimax Message Passing Algorithm Kye-Hyeon Kim, Seungjin Choi

Non-Isometric Manifold Learning: Analysis and an Algorithm Piotr Dollár, Vincent C. Rabaud, Serge J. Belongie

Nonlinear Independent Component Analysis with Minimal Nonlinear Distortion Kun Zhang, Laiwan Chan

Nonmyopic Active Learning of Gaussian Processes: An Exploration-Exploitation Approach Andreas Krause, Carlos Guestrin

On Learning Linear Ranking Functions for Beam Search Yuehua Xu, Alan Fern

On Learning with Dissimilarity Functions Liwei Wang, Cheng Yang, Jufu Feng

On One Method of Non-Diagonal Regularization in Sparse Bayesian Learning Dmitry Kropotov, Dmitry P. Vetrov

On the Relation Between Multi-Instance Learning and Semi-Supervised Learning Zhi-Hua Zhou, Jun-Ming Xu

On the Role of Tracking in Stationary Environments Richard S. Sutton, Anna Koop, David Silver

On the Value of Pairwise Constraints in Classification and Consistency Jian Zhang, Rong Yan

Online Discovery of Similarity Mappings Alexander Rakhlin, Jacob D. Abernethy, Peter L. Bartlett

Online Kernel PCA with Entropic Matrix Updates Dima Kuzmin, Manfred K. Warmuth

Optimal Dimensionality of Metric Space for Classification Wei Zhang, Xiangyang Xue, Zichen Sun, Yue-Fei Guo, Hong Lu

Parameter Learning for Relational Bayesian Networks Manfred Jaeger

Pegasos: Primal Estimated Sub-GrAdient SOlver for SVM Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro

Percentile Optimization in Uncertain Markov Decision Processes with Application to Efficient Exploration Erick Delage, Shie Mannor

Piecewise Pseudolikelihood for Efficient Training of Conditional Random Fields Charles Sutton, Andrew McCallum

Quadratically Gated Mixture of Experts for Incomplete Data Classification Xuejun Liao, Hui Li, Lawrence Carin

Quantum Clustering Algorithms Esma Aïmeur, Gilles Brassard, Sébastien Gambs

Recovering Temporally Rewiring Networks: A Model-Based Approach Fan Guo, Steve Hanneke, Wenjie Fu, Eric P. Xing

Regression on Manifolds Using Kernel Dimension Reduction Jens Nilsson, Fei Sha, Michael I. Jordan

Reinforcement Learning by Reward-Weighted Regression for Operational Space Control Jan Peters, Stefan Schaal

Relational Clustering by Symmetric Convex Coding Bo Long, Zhongfei (Mark) Zhang, Xiaoyun Wu, Philip S. Yu

Restricted Boltzmann Machines for Collaborative Filtering Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hinton

Revisiting Probabilistic Models for Clustering with Pair-Wise Constraints Blaine Nelson, Ira Cohen

Robust Mixtures in the Presence of Measurement Errors Jianyong Sun, Ata Kabán, Somak Raychaudhury

Robust Multi-Task Learning with T-Processes Shipeng Yu, Volker Tresp, Kai Yu

Robust Non-Linear Dimensionality Reduction Using Successive 1-Dimensional Laplacian Eigenmaps Samuel Gerber, Tolga Tasdizen, Ross T. Whitaker

Sample Compression Bounds for Decision Trees Mohak Shah

Scalable Modeling of Real Graphs Using Kronecker Multiplication Jure Leskovec, Christos Faloutsos

Scalable Training of L1-Regularized Log-Linear Models Galen Andrew, Jianfeng Gao

Self-Taught Learning: Transfer Learning from Unlabeled Data Rajat Raina, Alexis J. Battle, Honglak Lee, Benjamin Packer, Andrew Y. Ng

Simple, Robust, Scalable Semi-Supervised Learning via Expectation Regularization Gideon S. Mann, Andrew McCallum

Simpler Core Vector Machines with Enclosing Balls Ivor W. Tsang, András Kocsor, James T. Kwok

Solving Multiclass Support Vector Machines with LaRank Antoine Bordes, Léon Bottou, Patrick Gallinari, Jason Weston

Sparse Eigen Methods by D.C. Programming Bharath K. Sriperumbudur, David A. Torres, Gert R. G. Lanckriet

Sparse Probabilistic Classifiers Romain Hérault, Yves Grandvalet

Spectral Clustering and Transductive Learning with Multiple Views Dengyong Zhou, Christopher J. C. Burges

Spectral Feature Selection for Supervised and Unsupervised Learning Zheng Zhao, Huan Liu

Statistical Predicate Invention Stanley Kok, Pedro M. Domingos

Structural Alignment Based Kernels for Protein Structure Classification Sourangshu Bhattacharya, Chiranjib Bhattacharyya, Nagasuma R. Chandra

Supervised Clustering of Streaming Data for Email Batch Detection Peter Haider, Ulf Brefeld, Tobias Scheffer

Supervised Feature Selection via Dependence Estimation Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo

Support Cluster Machine Bin Li, Mingmin Chi, Jianping Fan, Xiangyang Xue

The Matrix Stick-Breaking Process for Flexible Multi-Task Learning Ya Xue, David B. Dunson, Lawrence Carin

The Rendezvous Algorithm: Multiclass Semi-Supervised Learning with Markov Random Walks Arik Azran

Three New Graphical Models for Statistical Language Modelling Andriy Mnih, Geoffrey E. Hinton

Tracking Value Function Dynamics to Improve Reinforcement Learning with Piecewise Linear Function Approximation Chee Wee Phua, Robert Fitch

Transductive Regression Piloted by Inter-Manifold Relations Huan Wang, Shuicheng Yan, Thomas S. Huang, Jianzhuang Liu, Xiaoou Tang

Transductive Support Vector Machines for Structured Variables Alexander Zien, Ulf Brefeld, Tobias Scheffer

Trust Region Newton Methods for Large-Scale Logistic Regression Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi

Two-View Feature Generation Model for Semi-Supervised Learning Rie Kubota Ando, Tong Zhang

Uncovering Shared Structures in Multiclass Classification Yonatan Amit, Michael Fink, Nathan Srebro, Shimon Ullman

Unsupervised Estimation for Noisy-Channel Models Markos Mylonakis, Khalil Sima'an, Rebecca Hwa

Unsupervised Prediction of Citation Influences Laura Dietz, Steffen Bickel, Tobias Scheffer

What Is Decreased by the Max-Sum Arc Consistency Algorithm? Tomás Werner

Winnowing Subspaces Manfred K. Warmuth