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All publications at Proceedings of the 26th International Conference on Machine Learning (ICML-09)
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SimpleNPKL: simple non-parametric kernel learning
Jinfeng Zhuang, Ivor W. Tsang and Steven C. Hoi
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

On primal and dual sparsity of Markov networks
Jun Zhu and Eric P. Xing
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

MedLDA: maximum margin supervised topic models for regression and classification
Jun Zhu, Amr Ahmed and Eric P. Xing
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Multi-instance learning by treating instances as non-I.I.D. samples
Zhi-hua Zhou, Yu-yin Sun and Yu-feng Li
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning non-redundant codebooks for classifying complex objects
Wei Zhang, Akshat Surve, Thomas G. Dietterich and Xiaoli Z. Fern
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Prototype vector machine for large scale semi-supervised learning
Kai Zhang, James T. Kwok and Bahram Parvin
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning instance specific distances using metric propagation
De-chuan Zhan, Ming Li, Yu-feng Li and Zhi-hua Zhou
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Discovering options from example trajectories
Peng Zang, Peng Zhou, David Minnen and Charles Jr.
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Compositional noisy-logical learning
Alan L. Yuille and Songfeng Zheng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Interactively optimizing information retrieval systems as a dueling bandits problem
Yisong Yue and Thorsten Joachims
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Robust feature extraction via information theoretic learning
Xiaotong Yuan and Bao-gang Hu
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Workshop summary: Workshop on learning feature hierarchies
Kay Yu, Ruslan Salakhutdinov, Yann Lecun, Geoffrey E. Hinton and Yoshua Bengio
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Piecewise-stationary bandit problems with side observations
Jia Y. Yu and Shie Mannor
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Large-scale collaborative prediction using a nonparametric random effects model
Kai Yu, John D. Lafferty, Shenghuo Zhu and Yihong Gong
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning structural SVMs with latent variables
Chun-nam J. Yu and Thorsten Joachims
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Stochastic search using the natural gradient
Yi Sun, Daan Wierstra, Tom Schaul and Jürgen Schmidhuber
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Online learning by ellipsoid method
Liu Yang, Rong Jin and Jieping Ye
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning
Linli Xu, Martha White and Dale Schuurmans
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Non-monotonic feature selection
Zenglin Xu, Rong Jin, Jieping Ye, Michael R. Lyu and Irwin King
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

A stochastic memoizer for sequence data
Frank Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James and Yee W. Teh
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Workshop summary: Results of the 2009 reinforcement learning competition
David Wingate, Carlos Diuk, Lihong Li, Matthew Taylor and Jordan Frank
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Herding dynamical weights to learn
Max Welling
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Feature hashing for large scale multitask learning
Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Josh Attenberg and Alex J. Smola
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Tutorial summary: Survey of boosting from an optimization perspective
Manfred K. Warmuth and S.v.n. Vishwanathan
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Evaluation methods for topic models
Hanna M. Wallach, Iain Murray, Ruslan Salakhutdinov and David M. Mimno
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

K-means in space: a radiation sensitivity evaluation
Kiri L. Wagstaff and Benjamin J. Bornstein
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

BoltzRank: learning to maximize expected ranking gain
Maksims Volkovs and Richard S. Zemel
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Model-free reinforcement learning as mixture learning
Marc Toussaint and Nikos A. Vlassis
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

More generality in efficient multiple kernel learning
Manik Varma and Bodla R. Babu
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Ranking with ordered weighted pairwise classification
Nicolas Usunier, David Buffoni and Patrick Gallinari
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Tutorial summary: Learning with dependencies between several response variables
Volker Tresp and Kai Yu
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Robot trajectory optimization using approximate inference
Marc Toussaint
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Structure learning with independent non-identically distributed data
Robert E. Tillman
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Using fast weights to improve persistent contrastive divergence
Tijmen Tieleman and Geoffrey E. Hinton
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Kernelized value function approximation for reinforcement learning
Gavin Taylor and Ronald Parr
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Factored conditional restricted Boltzmann Machines for modeling motion style
Graham W. Taylor and Geoffrey E. Hinton
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Discriminative {\it k}-metrics
Guillermo Sapiro and Arthur D. Szlam
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Optimistic initialization and greediness lead to polynomial time learning in factored MDPs
Istvan Szita and András Lörincz
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Fast gradient-descent methods for temporal-difference learning with linear function approximation
Richard S. Sutton, Hamid R. Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári and Eric Wiewiora
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

A simpler unified analysis of budget perceptrons
Ilya Sutskever
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

A least squares formulation for a class of generalized eigenvalue problems in machine learning
Liang Sun, Shuiwang Ji and Jieping Ye
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Multi-assignment clustering for Boolean data
Andreas P. Streich, Mario Frank, David A. Basin and Joachim M. Buhmann
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Hilbert space embeddings of conditional distributions with applications to dynamical systems
Le Song, Jonathan Huang, Kenji Fukumizu and Alex J. Smola
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Tutorial summary: Structured prediction for natural language processing
Noah A. Smith
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Uncertainty sampling and transductive experimental design for active dual supervision
Vikas Sindhwani, Prem Melville and Richard D. Lawrence
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Workshop summary: Abstraction in reinforcement learning
Özgür Simsek
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Monte-Carlo simulation balancing
David Silver and Gerald Tesauro
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Structure preserving embedding
Blake Shaw and Tony Jebara
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Stochastic methods for {\it l}$_{\mbox{1}}$ regularized loss minimization
Shai Shalev-shwartz and Ambuj Tewari
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Workshop summary: Numerical mathematics in machine learning
Matthias Seeger, Suvrit Sra and John P. Cunningham
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Function factorization using warped Gaussian processes
Mikkel N. Schmidt
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Ranking interesting subgroups
Stefan Rüping
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning structurally consistent undirected probabilistic graphical models
Sushmita Roy, Terran Lane and Margaret Werner-washburne
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Surrogate regret bounds for proper losses
Mark D. Reid and Robert C. Williamson
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Supervised learning from multiple experts: whom to trust when everyone lies a bit
Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Anna K. Jerebko, Charles Florin, Gerardo H. Valadez, Luca Bogoni and Linda Moy
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

The Bayesian group-Lasso for analyzing contingency tables
Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edgar Dahl and Volker Roth
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Large-scale deep unsupervised learning using graphics processors
Rajat Raina, Anand Madhavan and Andrew Y. Ng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Nearest neighbors in high-dimensional data: the emergence and influence of hubs
Milos Radovanovic, Alexandros Nanopoulos and Mirjana Ivanovic
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

An efficient projection for {\it l}$_{\mbox{1}}$,$_{\mbox{infinity}}$ regularization
Ariadna Quattoni, Xavier Carreras, Michael Collins and Trevor Darrell
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Sparse higher order conditional random fields for improved sequence labeling
Xian Qian, Xiaoqian Jiang, Qi Zhang, Xuanjing Huang and Lide Wu
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

An efficient sparse metric learning in high-dimensional space via {\it l}$_{\mbox{1}}$-penalized log-determinant regularization
Guo-jun Qi, Jinhui Tang, Zheng-jun Zha, Tat-seng Chua and Hong-jiang Zhang
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Independent factor topic models
Duangmanee Putthividhya, Hagai T. Attias and Srikantan S. Nagarajan
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning when to stop thinking and do something!
Barnabás Póczos, Yasin Abbasi-yadkori, Csaba Szepesvári, Russell Greiner and Nathan R. Sturtevant
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Multi-class image segmentation using conditional random fields and global classification
Nils Plath, Marc Toussaint and Shinichi Nakajima
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Constraint relaxation in approximate linear programs
Marek Petrik and Shlomo Zilberstein
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Detecting the direction of causal time series
Jonas Peters, Dominik Janzing, Arthur Gretton and Bernhard Schölkopf
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Binary action search for learning continuous-action control policies
Jason Pazis and Michail G. Lagoudakis
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Unsupervised hierarchical modeling of locomotion styles
Wei Pan and Lorenzo Torresani
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Nonparametric factor analysis with beta process priors
John W. Paisley and Lawrence Carin
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Solution stability in linear programming relaxations: graph partitioning and unsupervised learning
Sebastian Nowozin and Stefanie Jegelka
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Tutorial summary: The neuroscience of reinforcement learning
Yael Niv
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Convex variational Bayesian inference for large scale generalized linear models
Hannes Nickisch and Matthias Seeger
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Information theoretic measures for clusterings comparison: is a correction for chance necessary?
Xuan V. Nguyen, Julien Epps and James Bailey
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning complex motions by sequencing simpler motion templates
Gerhard Neumann, Wolfgang Maass and Jan R. Peters
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Workshop summary: Automated interpretation and modelling of cell images
Robert F. Murphy, Chun-nan Hsu and Loris Nanni
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Regression by dependence minimization and its application to causal inference in additive noise models
Joris M. Mooij, Dominik Janzing, Jonas Peters and Bernhard Schölkopf
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Deep learning from temporal coherence in video
Hossein Mobahi, Ronan Collobert and Jason Weston
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Bandit-based optimization on graphs with application to library performance tuning
Frédéric D. Mesmay, Arpad Rimmel, Yevgen Voronenko and Markus Püschel
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Partial order embedding with multiple kernels
Brian Mcfee and Gert R. Lanckriet
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Polyhedral outer approximations with application to natural language parsing
André Martins, Noah A. Smith and Eric P. Xing
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Sparse Gaussian graphical models with unknown block structure
Benjamin M. Marlin and Kevin P. Murphy
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Proto-predictive representation of states with simple recurrent temporal-difference networks
Takaki Makino
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Online dictionary learning for sparse coding
Julien Mairal, Jean Ponce, Guillermo Sapiro and Francis R. Bach
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Identifying suspicious URLs: an application of large-scale online learning
Justin Ma, Lawrence K. Saul, Stefan Savage and Geoffrey M. Voelker
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Geometry-aware metric learning
Zhengdong Lu, Prateek Jain and Inderjit S. Dhillon
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Efficient Euclidean projections in linear time
Jun Liu and Jieping Ye
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery
Han Liu, Mark Palatucci and Jian Zhang
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Topic-link LDA: joint models of topic and Author community
Yan Liu, Alexandru Niculescu-mizil and Wojciech Gryc
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning from measurements in exponential families
Percy Liang, Michael I. Jordan and Dan Klein
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Transfer learning for collaborative filtering via a rating-matrix generative model
Bin Li, Qiang Yang and Xiangyang Xue
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Semi-supervised learning using label mean
Yu-feng Li, James T. Kwok and Zhi-hua Zhou
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

ABC-boost: adaptive base class boost for multi-class classification
Ping Li
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Tutorial summary: Large social and information networks: opportunities for ML
Jure Leskovec
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Honglak Lee, Roger Grosse, Rajesh Ranganath and Andrew Y. Ng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Non-linear matrix factorization with Gaussian processes
Neil D. Lawrence and Raquel Urtasun
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning nonlinear dynamic models
John Langford, Ruslan Salakhutdinov and Tong Zhang
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Approximate inference for planning in stochastic relational worlds
Tobias Lang and Marc Toussaint
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Generalization analysis of listwise learning-to-rank algorithms
Yanyan Lan, Tie-yan Liu, Zhiming Ma and Hang Li
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties
Ondrej Kuzelka and Filip Zelezn\'y
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning spectral graph transformations for link prediction
Jérôme Kunegis and Andreas Lommatzsch
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

On sampling-based approximate spectral decomposition
Sanjiv Kumar, Mehryar Mohri and Ameet Talwalkar
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Multiple indefinite kernel learning with mixed norm regularization
Matthieu Kowalski, Marie Szafranski and Liva Ralaivola
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Rule learning with monotonicity constraints
Wojciech Kotlowski and Roman Slowinski
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

The graphlet spectrum
Risi Kondor, Nino Shervashidze and Karsten M. Borgwardt
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Regularization and feature selection in least-squares temporal difference learning
J. Z. Kolter and Andrew Y. Ng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Near-Bayesian exploration in polynomial time
J. Z. Kolter and Andrew Y. Ng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning Markov logic network structure via hypergraph lifting
Stanley Kok and Pedro Domingos
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Boosting products of base classifiers
Balázs Kégl and Róbert Busa-fekete
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning prediction suffix trees with Winnow
Nikolaos Karampatziakis and Dexter Kozen
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

A Bayesian approach to protein model quality assessment
Hetunandan Kamisetty and Christopher J. Langmead
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Orbit-product representation and correction of Gaussian belief propagation
Jason K. Johnson, Vladimir Y. Chernyak and Michael Chertkov
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

An accelerated gradient method for trace norm minimization
Shuiwang Ji and Jieping Ye
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Trajectory prediction: learning to map situations to robot trajectories
Nikolay Jetchev and Marc Toussaint
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Graph construction and {\it b}-matching for semi-supervised learning
Tony Jebara, Jun Wang and Shih-fu Chang
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Group lasso with overlap and graph lasso
Laurent Jacob, Jean-philippe Vert and Guillaume R. Obozinski
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning with structured sparsity
Junzhou Huang, Tong Zhang and Dimitris N. Metaxas
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning linear dynamical systems without sequence information
Tzu-kuo Huang and Jeff G. Schneider
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Partially supervised feature selection with regularized linear models
Thibault Helleputte and Pierre Dupont
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search
Verena Heidrich-meisner and Christian Igel
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Efficient learning algorithms for changing environments
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Bayesian clustering for email campaign detection
Peter Haider and Tobias Scheffer
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Bayesian inference for Plackett-Luce ranking models
John Guiver and Edward Snelson
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Dynamic analysis of multiagent {\it Q}-learning with {\&}epsilon;-greedy exploration
Eduardo R. Gomes and Ryszard Kowalczyk
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Fast evolutionary maximum margin clustering
Fabian Gieseke, Tapio Pahikkala and Oliver Kramer
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

PAC-Bayesian learning of linear classifiers
Pascal Germain, Alexandre Lacasse, Mario Marchand and François Laviolette
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Sequential Bayesian prediction in the presence of changepoints
Roman Garnett, Stephen J. Roberts and Michael Osborne
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property
Rahul Garg and Rohit Khandekar
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Dynamic mixed membership blockmodel for evolving networks
Wenjie Fu, Le Song and Eric P. Xing
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Invited talk: Drifting games, boosting and online learning
Yoav Freund
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

A majorization-minimization algorithm for (multiple) hyperparameter learning
Chuan-sheng Foo, Chuong B. Do and Andrew Y. Ng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

GAODE and HAODE: two proposals based on AODE to deal with continuous variables
M. J. Flores, José A. Gámez, Ana M. Mart\'ınez and Jose M. Puerta
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning to segment from a few well-selected training images
Alireza Farhangfar, Russell Greiner and Csaba Szepesvári
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Tutorial summary: Convergence of natural dynamics to equilibria
Eyal Even-dar and Vahab S. Mirrokni
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Workshop summary: Sparse methods for music audio
Douglas Eck, Dan Ellis and Philippe Hamel
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Boosting with structural sparsity
John C. Duchi and Yoram Singer
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Domain adaptation from multiple sources via auxiliary classifiers
Lixin Duan, Ivor W. Tsang, Dong Xu and Tat-seng Chua
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Workshop summary: The fourth workshop on evaluation methods for machine learning
Chris Drummond, Nathalie Japkowicz, William Klement and Sofus A. Macskassy
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Accounting for burstiness in topic models
Gabriel Doyle and Charles Elkan
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Accelerated sampling for the Indian Buffet Process
Finale Doshi-velez and Zoubin Ghahramani
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Proximal regularization for online and batch learning
Chuong B. Do, Quoc V. Le and Chuan-sheng Foo
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Large margin training for hidden Markov models with partially observed states
Trinh Do and Thierry Arti\`eres
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

The adaptive {\it k}-meteorologists problem and its application to structure learning and feature selection in reinforcement learning
Carlos Diuk, Lihong Li and Bethany R. Leffler
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

A scalable framework for discovering coherent co-clusters in noisy data
Meghana Deodhar, Gunjan Gupta, Joydeep Ghosh, Hyuk Cho and Inderjit S. Dhillon
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Good learners for evil teachers
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Analytic moment-based Gaussian process filtering
Marco F. Huber, Uwe D. Hanebeck and Marc Deisenroth
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Deep transfer via second-order Markov logic
Jesse Davis and Pedro Domingos
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Unsupervised search-based structured prediction
Hal Daume
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Tutorial summary: Active learning
Sanjoy Dasgupta and John Langford
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Fitting a graph to vector data
Samuel I. Daitch, Jonathan A. Kelner and Daniel A. Spielman
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

EigenTransfer: a unified framework for transfer learning
Wenyuan Dai, Ou Jin, Gui-rong Xue, Qiang Yang and Yong Yu
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Invited talk: Can learning kernels help performance?
Corinna Cortes
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Nonparametric estimation of the precision-recall curve
Nicolas Vayatis and Stéphan J. Clémençcon
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Exploiting sparse Markov {\it and} covariance structure in multiresolution models
Myung J. Choi, Venkat Chandrasekaran and Alan S. Willsky
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning dictionaries of stable autoregressive models for audio scene analysis
Youngmin Cho and Lawrence K. Saul
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Matrix updates for perceptron training of continuous density hidden Markov models
Chih-chieh Cheng, Fei Sha and Lawrence K. Saul
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Decision tree and instance-based learning for label ranking
Weiwei Cheng, Jens C. Huhn and Eyke Hüllermeier
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

A convex formulation for learning shared structures from multiple tasks
Jianhui Chen, Lei Tang, Jun Liu and Jieping Ye
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Learning kernels from indefinite similarities
Yihua Chen, Benjamin Recht and Maya Gupta
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Multi-view clustering via canonical correlation analysis
Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu and Karthik Sridharan
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Robust bounds for classification via selective sampling
Nicolò Cesa-bianchi, Claudio Gentile and Francesco Orabona
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Structure learning of Bayesian networks using constraints
Cassio Campos, Zhi Zeng and Qiang Ji
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Probabilistic dyadic data analysis with local and global consistency
Deng Cai, Xuanhui Wang and Xiaofei He
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Optimized expected information gain for nonlinear dynamical systems
Alberto G. Busetto, Cheng S. Ong and Joachim M. Buhmann
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Active learning for directed exploration of complex systems
Michael C. Burl and Esther Wang
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Spectral clustering based on the graph {\it p}-Laplacian
Thomas Bühler and Matthias Hein
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Online feature elicitation in interactive optimization
Craig Boutilier, Kevin Regan and Paolo Viappiani
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Predictive representations for policy gradient in POMDPs
Abdeslam Boularias and Brahim Chaib-draa
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Split variational inference
Guillaume Bouchard and Onno Zoeter
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Tutorial summary: Reductions in machine learning
Alina Beygelzimer, John Langford and Bianca Zadrozny
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Importance weighted active learning
Alina Beygelzimer, Sanjoy Dasgupta and John Langford
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Tutorial summary: Machine learning in IR: recent successes and new opportunities
Paul N. Bennett, Misha Bilenko and Kevyn Collins-thompson
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert and Jason Weston
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Grammatical inference as a principal component analysis problem
Rapha\"el Bailly, Françcois Denis and Liva Ralaivola
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Workshop summary: On-line learning with limited feedback
Jean-yves Audibert, Peter Auer, Alessandro Lazaric, Rémi Munos, Daniil Ryabko and Csaba Szepesvári
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Incorporating domain knowledge into topic modeling via Dirichlet Forest priors
David Andrzejewski, Xiaojin Zhu and Mark Craven
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Route kernels for trees
Fabio Aiolli, Giovanni Martino and Alessandro Sperduti
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Workshop summary: Seventh annual workshop on Bayes applications
John M. Agosta, Russell Almond, Dennis M. Buede, Marek J. Druzdzel, Judy Goldsmith and Silja Renooij
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities
Ryan P. Adams, Iain Murray and David Mackay
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009

Archipelago: nonparametric Bayesian semi-supervised learning
Ryan P. Adams and Zoubin Ghahramani
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009