26. ICML 2009: Montreal, Quebec, Canada

• Ryan Prescott Adams, Zoubin Ghahramani:
  Archipelago: nonparametric Bayesian semi-supervised learning. 1
  
• Ryan Prescott Adams, Iain Murray, David J. C. MacKay:
  Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. 2
  
• Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti:
  Route kernels for trees. 3
  
• David Andrzejewski, Xiaojin Zhu, Mark Craven:
  Incorporating domain knowledge into topic modeling via Dirichlet Forest priors. 4
  
• Raphaël Bailly, François Denis, Liva Ralaivola:
  Grammatical inference as a principal component analysis problem. 5
  
• Yoshua Bengio, Jérôme Louradour, Ronan Collobert, Jason Weston:
  Curriculum learning. 6
  
• Alina Beygelzimer, Sanjoy Dasgupta, John Langford:
  Importance weighted active learning. 7
  
• Guillaume Bouchard, Onno Zoeter:
  Split variational inference. 8
  
• Abdeslam Boularias, Brahim Chaib-draa:
  Predictive representations for policy gradient in POMDPs. 9
  
• Craig Boutilier, Kevin Regan, Paolo Viappiani:
  Online feature elicitation in interactive optimization. 10
  
• Thomas Bühler, Matthias Hein:
  Spectral clustering based on the graph p-Laplacian. 11
  
• Michael C. Burl, Esther Wang:
  Active learning for directed exploration of complex systems. 12
  
• Alberto Giovanni Busetto, Cheng Soon Ong, Joachim M. Buhmann:
  Optimized expected information gain for nonlinear dynamical systems. 13
  
• Deng Cai, Xuanhui Wang, Xiaofei He:
  Probabilistic dyadic data analysis with local and global consistency. 14
  
• Cassio Polpo de Campos, Zhi Zeng, Qiang Ji:
  Structure learning of Bayesian networks using constraints. 15
  
• Nicolò Cesa-Bianchi, Claudio Gentile, Francesco Orabona:
  Robust bounds for classification via selective sampling. 16
  
• Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu, Karthik Sridharan:
  Multi-view clustering via canonical correlation analysis. 17
  
• Jianhui Chen, Lei Tang, Jun Liu, Jieping Ye:
  A convex formulation for learning shared structures from multiple tasks. 18
  
• Yihua Chen, Maya R. Gupta, Benjamin Recht:
  Learning kernels from indefinite similarities. 19
  
• Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul:
  Matrix updates for perceptron training of continuous density hidden Markov models. 20
  
• Weiwei Cheng, Jens C. Huhn, Eyke Hüllermeier:
  Decision tree and instance-based learning for label ranking. 21
  
• Youngmin Cho, Lawrence K. Saul:
  Learning dictionaries of stable autoregressive models for audio scene analysis. 22
  
• Myung Jin Choi, Venkat Chandrasekaran, Alan S. Willsky:
  Exploiting sparse Markov and covariance structure in multiresolution models. 23
  
• Stéphan Clémençon, Nicolas Vayatis:
  Nonparametric estimation of the precision-recall curve. 24
  
• Wenyuan Dai, Ou Jin, Gui-Rong Xue, Qiang Yang, Yong Yu:
  EigenTransfer: a unified framework for transfer learning. 25
  
• Samuel I. Daitch, Jonathan A. Kelner, Daniel A. Spielman:
  Fitting a graph to vector data. 26
  
• Hal Daumé III:
  Unsupervised search-based structured prediction. 27
  
• Jesse Davis, Pedro Domingos:
  Deep transfer via second-order Markov logic. 28
  
• Marc Peter Deisenroth, Marco F. Huber, Uwe D. Hanebeck:
  Analytic moment-based Gaussian process filtering. 29
  
• Ofer Dekel, Ohad Shamir:
  Good learners for evil teachers. 30
  
• Meghana Deodhar, Gunjan Gupta, Joydeep Ghosh, Hyuk Cho, Inderjit S. Dhillon:
  A scalable framework for discovering coherent co-clusters in noisy data. 31
  
• Carlos Diuk, Lihong Li, Bethany R. Leffler:
  The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning. 32
  
• Chuong B. Do, Quoc V. Le, Chuan-Sheng Foo:
  Proximal regularization for online and batch learning. 33
  
• Trinh Minh Tri Do, Thierry Artières:
  Large margin training for hidden Markov models with partially observed states. 34
  
• Finale Doshi-Velez, Zoubin Ghahramani:
  Accelerated sampling for the Indian Buffet Process. 35
  
• Gabriel Doyle, Charles Elkan:
  Accounting for burstiness in topic models. 36
  
• Lixin Duan, Ivor W. Tsang, Dong Xu, Tat-Seng Chua:
  Domain adaptation from multiple sources via auxiliary classifiers. 37
  
• John C. Duchi, Yoram Singer:
  Boosting with structural sparsity. 38
  
• Alireza Farhangfar, Russell Greiner, Csaba Szepesvári:
  Learning to segment from a few well-selected training images. 39
  
• M. Julia Flores, José A. Gámez, Ana M. Martínez, Jose Miguel Puerta:
  GAODE and HAODE: two proposals based on AODE to deal with continuous variables. 40
  
• Chuan-Sheng Foo, Chuong B. Do, Andrew Y. Ng:
  A majorization-minimization algorithm for (multiple) hyperparameter learning. 41
  
• Wenjie Fu, Le Song, Eric P. Xing:
  Dynamic mixed membership blockmodel for evolving networks. 42
  
• Rahul Garg, Rohit Khandekar:
  Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property. 43
  
• Roman Garnett, Michael A. Osborne, Stephen J. Roberts:
  Sequential Bayesian prediction in the presence of changepoints. 44
  
• Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand:
  PAC-Bayesian learning of linear classifiers. 45
  
• Fabian Gieseke, Tapio Pahikkala, Oliver Kramer:
  Fast evolutionary maximum margin clustering. 46
  
• Eduardo Rodrigues Gomes, Ryszard Kowalczyk:
  Dynamic analysis of multiagent Q-learning with ε-greedy exploration. 47
  
• John Guiver, Edward Snelson:
  Bayesian inference for Plackett-Luce ranking models. 48
  
• Peter Haider, Tobias Scheffer:
  Bayesian clustering for email campaign detection. 49
  
• Elad Hazan, C. Seshadhri:
  Efficient learning algorithms for changing environments. 50
  
• Verena Heidrich-Meisner, Christian Igel:
  Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search. 51
  
• Thibault Helleputte, Pierre Dupont:
  Partially supervised feature selection with regularized linear models. 52
  
• Junzhou Huang, Tong Zhang, Dimitris N. Metaxas:
  Learning with structured sparsity. 53
  
• Tzu-Kuo Huang, Jeff G. Schneider:
  Learning linear dynamical systems without sequence information. 54
  
• Laurent Jacob, Guillaume Obozinski, Jean-Philippe Vert:
  Group lasso with overlap and graph lasso. 55
  
• Tony Jebara, Jun Wang, Shih-Fu Chang:
  Graph construction and b-matching for semi-supervised learning. 56
  
• Nikolay Jetchev, Marc Toussaint:
  Trajectory prediction: learning to map situations to robot trajectories. 57
  
• Shuiwang Ji, Jieping Ye:
  An accelerated gradient method for trace norm minimization. 58
  
• This paper has been removed from the ICML 2009 proceedings due to a violation of ICML's dual-submission policy.
• Jason K. Johnson, Vladimir Y. Chernyak, Michael Chertkov:
  Orbit-product representation and correction of Gaussian belief propagation. 60
  
• Hetunandan Kamisetty, Christopher James Langmead:
  A Bayesian approach to protein model quality assessment. 61
  
• Nikolaos Karampatziakis, Dexter Kozen:
  Learning prediction suffix trees with Winnow. 62
  
• Balázs Kégl, Róbert Busa-Fekete:
  Boosting products of base classifiers. 63
  
• Stanley Kok, Pedro Domingos:
  Learning Markov logic network structure via hypergraph lifting. 64
  
• J. Zico Kolter, Andrew Y. Ng:
  Near-Bayesian exploration in polynomial time. 65
  
• J. Zico Kolter, Andrew Y. Ng:
  Regularization and feature selection in least-squares temporal difference learning. 66
  
• Risi Kondor, Nino Shervashidze, Karsten M. Borgwardt:
  The graphlet spectrum. 67
  
• Wojciech Kotlowski, Roman Slowinski:
  Rule learning with monotonicity constraints. 68
  
• Matthieu Kowalski, Marie Szafranski, Liva Ralaivola:
  Multiple indefinite kernel learning with mixed norm regularization. 69
  
• Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar:
  On sampling-based approximate spectral decomposition. 70
  
• Jérôme Kunegis, Andreas Lommatzsch:
  Learning spectral graph transformations for link prediction. 71
  
• Ondrej Kuzelka, Filip Zelezný:
  Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties. 72
  
• Yanyan Lan, Tie-Yan Liu, Zhiming Ma, Hang Li:
  Generalization analysis of listwise learning-to-rank algorithms. 73
  
• Tobias Lang, Marc Toussaint:
  Approximate inference for planning in stochastic relational worlds. 74
  
• John Langford, Ruslan Salakhutdinov, Tong Zhang:
  Learning nonlinear dynamic models. 75
  
• Neil D. Lawrence, Raquel Urtasun:
  Non-linear matrix factorization with Gaussian processes. 76
  
• Honglak Lee, Roger Grosse, Rajesh Ranganath, Andrew Y. Ng:
  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. 77
  
• Bin Li, Qiang Yang, Xiangyang Xue:
  Transfer learning for collaborative filtering via a rating-matrix generative model. 78
  
• Ping Li:
  ABC-boost: adaptive base class boost for multi-class classification. 79
  
• Yu-Feng Li, James T. Kwok, Zhi-Hua Zhou:
  Semi-supervised learning using label mean. 80
  
• Percy Liang, Michael I. Jordan, Dan Klein:
  Learning from measurements in exponential families. 81
  
• Han Liu, Mark Palatucci, Jian Zhang:
  Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery. 82
  
• Jun Liu, Jieping Ye:
  Efficient Euclidean projections in linear time. 83
  
• Yan Liu, Alexandru Niculescu-Mizil, Wojciech Gryc:
  Topic-link LDA: joint models of topic and author community. 84
  
• Zhengdong Lu, Prateek Jain, Inderjit S. Dhillon:
  Geometry-aware metric learning. 85
  
• Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker:
  Identifying suspicious URLs: an application of large-scale online learning. 86
  
• Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro:
  Online dictionary learning for sparse coding. 87
  
• Takaki Makino:
  Proto-predictive representation of states with simple recurrent temporal-difference networks. 88
  
• Benjamin M. Marlin, Kevin P. Murphy:
  Sparse Gaussian graphical models with unknown block structure. 89
  
• André F. T. Martins, Noah A. Smith, Eric P. Xing:
  Polyhedral outer approximations with application to natural language parsing. 90
  
• Brian McFee, Gert R. G. Lanckriet:
  Partial order embedding with multiple kernels. 91
  
• Frédéric de Mesmay, Arpad Rimmel, Yevgen Voronenko, Markus Püschel:
  Bandit-based optimization on graphs with application to library performance tuning. 92
  
• Hossein Mobahi, Ronan Collobert, Jason Weston:
  Deep learning from temporal coherence in video. 93
  
• Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf:
  Regression by dependence minimization and its application to causal inference in additive noise models. 94
  
• Gerhard Neumann, Wolfgang Maass, Jan Peters:
  Learning complex motions by sequencing simpler motion templates. 95
  
• Hannes Nickisch, Matthias W. Seeger:
  Convex variational Bayesian inference for large scale generalized linear models. 96
  
• Sebastian Nowozin, Stefanie Jegelka:
  Solution stability in linear programming relaxations: graph partitioning and unsupervised learning. 97
  
• John William Paisley, Lawrence Carin:
  Nonparametric factor analysis with beta process priors. 98
  
• Wei Pan, Lorenzo Torresani:
  Unsupervised hierarchical modeling of locomotion styles. 99
  
• Jason Pazis, Michail G. Lagoudakis:
  Binary action search for learning continuous-action control policies. 100
  
• Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf:
  Detecting the direction of causal time series. 101
  
• Marek Petrik, Shlomo Zilberstein:
  Constraint relaxation in approximate linear programs. 102
  
• Nils Plath, Marc Toussaint, Shinichi Nakajima:
  Multi-class image segmentation using conditional random fields and global classification. 103
  
• Barnabás Póczos, Yasin Abbasi-Yadkori, Csaba Szepesvári, Russell Greiner, Nathan R. Sturtevant:
  Learning when to stop thinking and do something! 104
  
• Duangmanee Putthividhya, Hagai Thomas Attias, Srikantan S. Nagarajan:
  Independent factor topic models. 105
  
• Guo-Jun Qi, Jinhui Tang, Zheng-Jun Zha, Tat-Seng Chua, Hong-Jiang Zhang:
  An efficient sparse metric learning in high-dimensional space via l₁-penalized log-determinant regularization. 106
  
• Xian Qian, Xiaoqian Jiang, Qi Zhang, Xuanjing Huang, Lide Wu:
  Sparse higher order conditional random fields for improved sequence labeling. 107
  
• Ariadna Quattoni, Xavier Carreras, Michael Collins, Trevor Darrell:
  An efficient projection for l₁,_infinity regularization. 108
  
• Milos Radovanovic, Alexandros Nanopoulos, Mirjana Ivanovic:
  Nearest neighbors in high-dimensional data: the emergence and influence of hubs. 109
  
• Rajat Raina, Anand Madhavan, Andrew Y. Ng:
  Large-scale deep unsupervised learning using graphics processors. 110
  
• Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edgar Dahl, Volker Roth:
  The Bayesian group-Lasso for analyzing contingency tables. 111
  
• Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Anna K. Jerebko, Charles Florin, Gerardo Hermosillo Valadez, Luca Bogoni, Linda Moy:
  Supervised learning from multiple experts: whom to trust when everyone lies a bit. 112
  
• Mark D. Reid, Robert C. Williamson:
  Surrogate regret bounds for proper losses. 113
  
• Sushmita Roy, Terran Lane, Margaret Werner-Washburne:
  Learning structurally consistent undirected probabilistic graphical models. 114
  
• Stefan Rüping:
  Ranking interesting subgroups. 115
  
• Mikkel N. Schmidt:
  Function factorization using warped Gaussian processes. 116
  
• Shai Shalev-Shwartz, Ambuj Tewari:
  Stochastic methods for l₁ regularized loss minimization. 117
  
• Blake Shaw, Tony Jebara:
  Structure preserving embedding. 118
  
• David Silver, Gerald Tesauro:
  Monte-Carlo simulation balancing. 119
  
• Vikas Sindhwani, Prem Melville, Richard D. Lawrence:
  Uncertainty sampling and transductive experimental design for active dual supervision. 120
  
• Le Song, Jonathan Huang, Alexander J. Smola, Kenji Fukumizu:
  Hilbert space embeddings of conditional distributions with applications to dynamical systems. 121
  
• Andreas P. Streich, Mario Frank, David A. Basin, Joachim M. Buhmann:
  Multi-assignment clustering for Boolean data. 122
  
• Liang Sun, Shuiwang Ji, Jieping Ye:
  A least squares formulation for a class of generalized eigenvalue problems in machine learning. 123
  
• Ilya Sutskever:
  A simpler unified analysis of budget perceptrons. 124
  
• Richard S. Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, Eric Wiewiora:
  Fast gradient-descent methods for temporal-difference learning with linear function approximation. 125
  
• Istvan Szita, András Lörincz:
  Optimistic initialization and greediness lead to polynomial time learning in factored MDPs. 126
  
• Arthur Szlam, Guillermo Sapiro:
  Discriminative k-metrics. 127
  
• Gavin Taylor, Ronald Parr:
  Kernelized value function approximation for reinforcement learning. 128
  
• Graham W. Taylor, Geoffrey E. Hinton:
  Factored conditional restricted Boltzmann Machines for modeling motion style. 129
  
• Tijmen Tieleman, Geoffrey E. Hinton:
  Using fast weights to improve persistent contrastive divergence. 130
  
• Robert E. Tillman:
  Structure learning with independent non-identically distributed data. 131
  
• Marc Toussaint:
  Robot trajectory optimization using approximate inference. 132
  
• Nicolas Usunier, David Buffoni, Patrick Gallinari:
  Ranking with ordered weighted pairwise classification. 133
  
• Manik Varma, Bodla Rakesh Babu:
  More generality in efficient multiple kernel learning. 134
  
• Xuan Vinh Nguyen, Julien Epps, James Bailey:
  Information theoretic measures for clusterings comparison: is a correction for chance necessary? 135
  
• Nikos Vlassis, Marc Toussaint:
  Model-free reinforcement learning as mixture learning. 136
  
• Maksims Volkovs, Richard S. Zemel:
  BoltzRank: learning to maximize expected ranking gain. 137
  
• Kiri L. Wagstaff, Benjamin J. Bornstein:
  K-means in space: a radiation sensitivity evaluation. 138
  
• Hanna M. Wallach, Iain Murray, Ruslan Salakhutdinov, David M. Mimno:
  Evaluation methods for topic models. 139
  
• Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, Josh Attenberg:
  Feature hashing for large scale multitask learning. 140
  
• Max Welling:
  Herding dynamical weights to learn. 141
  
• Frank Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh:
  A stochastic memoizer for sequence data. 142
  
• Linli Xu, Martha White, Dale Schuurmans:
  Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning. 143
  
• Zenglin Xu, Rong Jin, Jieping Ye, Michael R. Lyu, Irwin King:
  Non-monotonic feature selection. 144
  
• Liu Yang, Rong Jin, Jieping Ye:
  Online learning by ellipsoid method. 145
  
• Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber:
  Stochastic search using the natural gradient. 146
  
• Chun-Nam John Yu, Thorsten Joachims:
  Learning structural SVMs with latent variables. 147
  
• Jia Yuan Yu, Shie Mannor:
  Piecewise-stationary bandit problems with side observations. 148
  
• Kai Yu, John D. Lafferty, Shenghuo Zhu, Yihong Gong:
  Large-scale collaborative prediction using a nonparametric random effects model. 149
  
• Xiaotong Yuan, Bao-Gang Hu:
  Robust feature extraction via information theoretic learning. 150
  
• Yisong Yue, Thorsten Joachims:
  Interactively optimizing information retrieval systems as a dueling bandits problem. 151
  
• Alan L. Yuille, Songfeng Zheng:
  Compositional noisy-logical learning. 152
  
• Peng Zang, Peng Zhou, David Minnen, Charles Lee Isbell Jr.:
  Discovering options from example trajectories. 153
  
• De-Chuan Zhan, Ming Li, Yu-Feng Li, Zhi-Hua Zhou:
  Learning instance specific distances using metric propagation. 154
  
• Kai Zhang, James T. Kwok, Bahram Parvin:
  Prototype vector machine for large scale semi-supervised learning. 155
  
• Wei Zhang, Akshat Surve, Xiaoli Fern, Thomas G. Dietterich:
  Learning non-redundant codebooks for classifying complex objects. 156
  
• Zhi-Hua Zhou, Yu-Yin Sun, Yu-Feng Li:
  Multi-instance learning by treating instances as non-I.I.D. samples. 157
  
• Jun Zhu, Amr Ahmed, Eric P. Xing:
  MedLDA: maximum margin supervised topic models for regression and classification. 158
  
• Jun Zhu, Eric P. Xing:
  On primal and dual sparsity of Markov networks. 159
  
• Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi:
  SimpleNPKL: simple non-parametric kernel learning. 160
  
• Corinna Cortes:
  Invited talk: Can learning kernels help performance? 161
  
• Yoav Freund:
  Invited talk: Drifting games, boosting and online learning. 162
  
• John Mark Agosta, Russell Almond, Dennis M. Buede, Marek J. Druzdzel, Judy Goldsmith, Silja Renooij:
  Workshop summary: Seventh annual workshop on Bayes applications. 163
  
• Robert F. Murphy, Chun-Nan Hsu, Loris Nanni:
  Workshop summary: Automated interpretation and modelling of cell images. 164
  
• Kay Yu, Ruslan Salakhutdinov, Yann LeCun, Geoffrey E. Hinton, Yoshua Bengio:
  Workshop summary: Workshop on learning feature hierarchies. 165
  
• David Wingate, Carlos Diuk, Lihong Li, Matthew Taylor, Jordan Frank:
  Workshop summary: Results of the 2009 reinforcement learning competition. 166
  
• Chris Drummond, Nathalie Japkowicz, William Klement, Sofus A. Macskassy:
  Workshop summary: The fourth workshop on evaluation methods for machine learning. 167
  
• Jean-Yves Audibert, Peter Auer, Alessandro Lazaric, Rémi Munos, Daniil Ryabko, Csaba Szepesvári:
  Workshop summary: On-line learning with limited feedback. 168
  
• Matthias Seeger, Suvrit Sra, John P. Cunningham:
  Workshop summary: Numerical mathematics in machine learning. 169
  
• Özgür Simsek:
  Workshop summary: Abstraction in reinforcement learning. 170
  
• Douglas Eck, Dan Ellis, Philippe Hamel:
  Workshop summary: Sparse methods for music audio. 171
  
• Alina Beygelzimer, John Langford, Bianca Zadrozny:
  Tutorial summary: Reductions in machine learning. 172
  
• Eyal Even-Dar, Vahab S. Mirrokni:
  Tutorial summary: Convergence of natural dynamics to equilibria. 173
  
• Volker Tresp, Kai Yu:
  Tutorial summary: Learning with dependencies between several response variables. 174
  
• Manfred K. Warmuth, S. V. N. Vishwanathan:
  Tutorial summary: Survey of boosting from an optimization perspective. 175
  
• Yael Niv:
  Tutorial summary: The neuroscience of reinforcement learning. 176
  
• Paul N. Bennett, Misha Bilenko, Kevyn Collins-Thompson:
  Tutorial summary: Machine learning in IR: recent successes and new opportunities. 177
  
• Sanjoy Dasgupta, John Langford:
  Tutorial summary: Active learning. 178
  
• Jure Leskovec:
  Tutorial summary: Large social and information networks: opportunities for ML. 179
  
• Noah A. Smith:
  Tutorial summary: Structured prediction for natural language processing. 180
  

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