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ORCID26th ICML 2009: Montreal, Quebec, Canada
- Andrea Pohoreckyj Danyluk, Léon Bottou, Michael L. Littman:
Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009. ACM International Conference Proceeding Series 382, ACM 2009, ISBN 978-1-60558-516-1 - Ryan Prescott Adams, Zoubin Ghahramani:
Archipelago: nonparametric Bayesian semi-supervised learning. 1-8 - Ryan Prescott Adams, Iain Murray, David J. C. MacKay:
Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. 9-16 - Fabio Aiolli, Giovanni Da San Martino
, Alessandro Sperduti:
Route kernels for trees. 17-24 - David Andrzejewski, Xiaojin Zhu, Mark Craven:
Incorporating domain knowledge into topic modeling via Dirichlet Forest priors. 25-32 - Raphaël Bailly, François Denis, Liva Ralaivola:
Grammatical inference as a principal component analysis problem. 33-40 - Yoshua Bengio, Jérôme Louradour, Ronan Collobert, Jason Weston:
Curriculum learning. 41-48 - Alina Beygelzimer, Sanjoy Dasgupta, John Langford:
Importance weighted active learning. 49-56 - Guillaume Bouchard, Onno Zoeter:
Split variational inference. 57-64 - Abdeslam Boularias, Brahim Chaib-draa:
Predictive representations for policy gradient in POMDPs. 65-72 - Craig Boutilier, Kevin Regan, Paolo Viappiani:
Online feature elicitation in interactive optimization. 73-80 - Thomas Bühler, Matthias Hein:
Spectral clustering based on the graph p-Laplacian. 81-88 - Michael C. Burl, Esther Wang:
Active learning for directed exploration of complex systems. 89-96 - Alberto Giovanni Busetto, Cheng Soon Ong, Joachim M. Buhmann:
Optimized expected information gain for nonlinear dynamical systems. 97-104 - Deng Cai, Xuanhui Wang, Xiaofei He:
Probabilistic dyadic data analysis with local and global consistency. 105-112 - Cassio P. de Campos, Zhi Zeng, Qiang Ji:
Structure learning of Bayesian networks using constraints. 113-120 - Nicolò Cesa-Bianchi, Claudio Gentile, Francesco Orabona:
Robust bounds for classification via selective sampling. 121-128 - Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu, Karthik Sridharan:
Multi-view clustering via canonical correlation analysis. 129-136 - Jianhui Chen, Lei Tang, Jun Liu, Jieping Ye:
A convex formulation for learning shared structures from multiple tasks. 137-144 - Yihua Chen, Maya R. Gupta, Benjamin Recht:
Learning kernels from indefinite similarities. 145-152 - Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul:
Matrix updates for perceptron training of continuous density hidden Markov models. 153-160 - Weiwei Cheng, Jens C. Huhn, Eyke Hüllermeier:
Decision tree and instance-based learning for label ranking. 161-168 - Youngmin Cho, Lawrence K. Saul:
Learning dictionaries of stable autoregressive models for audio scene analysis. 169-176 - Myung Jin Choi, Venkat Chandrasekaran, Alan S. Willsky:
Exploiting sparse Markov and covariance structure in multiresolution models. 177-184 - Stéphan Clémençon, Nicolas Vayatis:
Nonparametric estimation of the precision-recall curve. 185-192 - Wenyuan Dai, Ou Jin, Gui-Rong Xue, Qiang Yang, Yong Yu:
EigenTransfer: a unified framework for transfer learning. 193-200 - Samuel I. Daitch, Jonathan A. Kelner, Daniel A. Spielman:
Fitting a graph to vector data. 201-208 - Hal Daumé III:
Unsupervised search-based structured prediction. 209-216 - Jesse Davis, Pedro M. Domingos:
Deep transfer via second-order Markov logic. 217-224 - Marc Peter Deisenroth, Marco F. Huber, Uwe D. Hanebeck:
Analytic moment-based Gaussian process filtering. 225-232 - Ofer Dekel, Ohad Shamir:
Good learners for evil teachers. 233-240 - Meghana Deodhar, Gunjan Gupta, Joydeep Ghosh, Hyuk Cho, Inderjit S. Dhillon:
A scalable framework for discovering coherent co-clusters in noisy data. 241-248 - Carlos Diuk, Lihong Li, Bethany R. Leffler:
The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning. 249-256 - Chuong B. Do, Quoc V. Le, Chuan-Sheng Foo:
Proximal regularization for online and batch learning. 257-264 - Trinh Minh Tri Do, Thierry Artières:
Large margin training for hidden Markov models with partially observed states. 265-272 - Finale Doshi-Velez, Zoubin Ghahramani:
Accelerated sampling for the Indian Buffet Process. 273-280 - Gabriel Doyle, Charles Elkan:
Accounting for burstiness in topic models. 281-288 - Lixin Duan, Ivor W. Tsang, Dong Xu, Tat-Seng Chua:
Domain adaptation from multiple sources via auxiliary classifiers. 289-296 - John C. Duchi, Yoram Singer:
Boosting with structural sparsity. 297-304 - Alireza Farhangfar, Russell Greiner, Csaba Szepesvári:
Learning to segment from a few well-selected training images. 305-312 - M. Julia Flores, José A. Gámez, Ana M. Martínez, José M. Puerta:
GAODE and HAODE: two proposals based on AODE to deal with continuous variables. 313-320 - Chuan-Sheng Foo, Chuong B. Do, Andrew Y. Ng:
A majorization-minimization algorithm for (multiple) hyperparameter learning. 321-328 - Wenjie Fu, Le Song, Eric P. Xing:
Dynamic mixed membership blockmodel for evolving networks. 329-336 - Rahul Garg, Rohit Khandekar:
Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property. 337-344 - Roman Garnett, Michael A. Osborne, Stephen J. Roberts:
Sequential Bayesian prediction in the presence of changepoints. 345-352 - Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand:
PAC-Bayesian learning of linear classifiers. 353-360 - Fabian Gieseke, Tapio Pahikkala, Oliver Kramer:
Fast evolutionary maximum margin clustering. 361-368 - Eduardo Rodrigues Gomes, Ryszard Kowalczyk:
Dynamic analysis of multiagent Q-learning with ε-greedy exploration. 369-376 - John Guiver, Edward Lloyd Snelson:
Bayesian inference for Plackett-Luce ranking models. 377-384 - Peter Haider, Tobias Scheffer:
Bayesian clustering for email campaign detection. 385-392 - Elad Hazan, C. Seshadhri:
Efficient learning algorithms for changing environments. 393-400 - Verena Heidrich-Meisner, Christian Igel:
Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search. 401-408 - Thibault Helleputte, Pierre Dupont:
Partially supervised feature selection with regularized linear models. 409-416 - Junzhou Huang, Tong Zhang, Dimitris N. Metaxas:
Learning with structured sparsity. 417-424 - Tzu-Kuo Huang, Jeff G. Schneider:
Learning linear dynamical systems without sequence information. 425-432 - Laurent Jacob, Guillaume Obozinski, Jean-Philippe Vert:
Group lasso with overlap and graph lasso. 433-440 - Tony Jebara, Jun Wang, Shih-Fu Chang:
Graph construction and b-matching for semi-supervised learning. 441-448 - Nikolay Jetchev, Marc Toussaint:
Trajectory prediction: learning to map situations to robot trajectories. 449-456 - Shuiwang Ji, Jieping Ye:
An accelerated gradient method for trace norm minimization. 457-464 - (Withdrawn) A novel lexicalized HMM-based learning framework for web opinion mining. 465-472
- Jason K. Johnson, Vladimir Y. Chernyak, Michael Chertkov:
Orbit-product representation and correction of Gaussian belief propagation. 473-480 - Hetunandan Kamisetty, Christopher James Langmead:
A Bayesian approach to protein model quality assessment. 481-488 - Nikolaos Karampatziakis, Dexter Kozen:
Learning prediction suffix trees with Winnow. 489-496 - Balázs Kégl, Róbert Busa-Fekete:
Boosting products of base classifiers. 497-504 - Stanley Kok, Pedro M. Domingos:
Learning Markov logic network structure via hypergraph lifting. 505-512 - J. Zico Kolter, Andrew Y. Ng:
Near-Bayesian exploration in polynomial time. 513-520 - J. Zico Kolter, Andrew Y. Ng:
Regularization and feature selection in least-squares temporal difference learning. 521-528 - Risi Kondor, Nino Shervashidze, Karsten M. Borgwardt:
The graphlet spectrum. 529-536 - Wojciech Kotlowski, Roman Slowinski:
Rule learning with monotonicity constraints. 537-544 - Matthieu Kowalski, Marie Szafranski, Liva Ralaivola:
Multiple indefinite kernel learning with mixed norm regularization. 545-552 - Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar:
On sampling-based approximate spectral decomposition. 553-560 - Jérôme Kunegis, Andreas Lommatzsch:
Learning spectral graph transformations for link prediction. 561-568 - Ondrej Kuzelka, Filip Zelezný:
Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties. 569-576 - Yanyan Lan, Tie-Yan Liu, Zhiming Ma, Hang Li:
Generalization analysis of listwise learning-to-rank algorithms. 577-584 - Tobias Lang, Marc Toussaint:
Approximate inference for planning in stochastic relational worlds. 585-592 - John Langford, Ruslan Salakhutdinov, Tong Zhang:
Learning nonlinear dynamic models. 593-600 - Neil D. Lawrence, Raquel Urtasun:
Non-linear matrix factorization with Gaussian processes. 601-608 - Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng:
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. 609-616 - Bin Li, Qiang Yang, Xiangyang Xue:
Transfer learning for collaborative filtering via a rating-matrix generative model. 617-624 - Ping Li:
ABC-boost: adaptive base class boost for multi-class classification. 625-632 - Yufeng Li, James T. Kwok, Zhi-Hua Zhou:
Semi-supervised learning using label mean. 633-640 - Percy Liang, Michael I. Jordan, Dan Klein:
Learning from measurements in exponential families. 641-648 - Han Liu, Mark Palatucci, Jian Zhang:
Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery. 649-656 - Jun Liu, Jieping Ye:
Efficient Euclidean projections in linear time. 657-664 - Yan Liu, Alexandru Niculescu-Mizil, Wojciech Gryc:
Topic-link LDA: joint models of topic and author community. 665-672 - Zhengdong Lu, Prateek Jain, Inderjit S. Dhillon:
Geometry-aware metric learning. 673-680 - Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker:
Identifying suspicious URLs: an application of large-scale online learning. 681-688 - Julien Mairal, Francis R. Bach, Jean Ponce, Guillermo Sapiro:
Online dictionary learning for sparse coding. 689-696 - Takaki Makino:
Proto-predictive representation of states with simple recurrent temporal-difference networks. 697-704 - Benjamin M. Marlin, Kevin P. Murphy:
Sparse Gaussian graphical models with unknown block structure. 705-712 - André F. T. Martins, Noah A. Smith, Eric P. Xing:
Polyhedral outer approximations with application to natural language parsing. 713-720 - Brian McFee, Gert R. G. Lanckriet:
Partial order embedding with multiple kernels. 721-728 - Frédéric de Mesmay, Arpad Rimmel, Yevgen Voronenko, Markus Püschel:
Bandit-based optimization on graphs with application to library performance tuning. 729-736 - Hossein Mobahi, Ronan Collobert, Jason Weston:
Deep learning from temporal coherence in video. 737-744 - Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf:
Regression by dependence minimization and its application to causal inference in additive noise models. 745-752 - Gerhard Neumann, Wolfgang Maass, Jan Peters:
Learning complex motions by sequencing simpler motion templates. 753-760 - Hannes Nickisch, Matthias W. Seeger:
Convex variational Bayesian inference for large scale generalized linear models. 761-768 - Sebastian Nowozin, Stefanie Jegelka:
Solution stability in linear programming relaxations: graph partitioning and unsupervised learning. 769-776 - John W. Paisley, Lawrence Carin:
Nonparametric factor analysis with beta process priors. 777-784 - Wei Pan, Lorenzo Torresani:
Unsupervised hierarchical modeling of locomotion styles. 785-792 - Jason Pazis, Michail G. Lagoudakis:
Binary action search for learning continuous-action control policies. 793-800 - Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf:
Detecting the direction of causal time series. 801-808 - Marek Petrik, Shlomo Zilberstein:
Constraint relaxation in approximate linear programs. 809-816 - Nils Plath, Marc Toussaint, Shinichi Nakajima:
Multi-class image segmentation using conditional random fields and global classification. 817-824 - Barnabás Póczos, Yasin Abbasi-Yadkori, Csaba Szepesvári, Russell Greiner, Nathan R. Sturtevant:
Learning when to stop thinking and do something! 825-832 - Duangmanee Putthividhya, Hagai Thomas Attias, Srikantan S. Nagarajan:
Independent factor topic models. 833-840 - 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. 841-848 - Xian Qian, Xiaoqian Jiang, Qi Zhang, Xuanjing Huang, Lide Wu:
Sparse higher order conditional random fields for improved sequence labeling. 849-856 - Ariadna Quattoni, Xavier Carreras, Michael Collins, Trevor Darrell:
An efficient projection for l1,infinity regularization. 857-864 - Milos Radovanovic, Alexandros Nanopoulos, Mirjana Ivanovic:
Nearest neighbors in high-dimensional data: the emergence and influence of hubs. 865-872 - Rajat Raina, Anand Madhavan, Andrew Y. Ng:
Large-scale deep unsupervised learning using graphics processors. 873-880 - Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edgar Dahl, Volker Roth:
The Bayesian group-Lasso for analyzing contingency tables. 881-888 - 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. 889-896 - Mark D. Reid, Robert C. Williamson:
Surrogate regret bounds for proper losses. 897-904 - Sushmita Roy, Terran Lane, Margaret Werner-Washburne:
Learning structurally consistent undirected probabilistic graphical models. 905-912 - Stefan Rüping:
Ranking interesting subgroups. 913-920 - Mikkel N. Schmidt:
Function factorization using warped Gaussian processes. 921-928 - Shai Shalev-Shwartz, Ambuj Tewari:
Stochastic methods for l1 regularized loss minimization. 929-936 - Blake Shaw, Tony Jebara:
Structure preserving embedding. 937-944 - David Silver, Gerald Tesauro:
Monte-Carlo simulation balancing. 945-952 - Vikas Sindhwani, Prem Melville, Richard D. Lawrence:
Uncertainty sampling and transductive experimental design for active dual supervision. 953-960 - Le Song, Jonathan Huang, Alexander J. Smola, Kenji Fukumizu:
Hilbert space embeddings of conditional distributions with applications to dynamical systems. 961-968 - Andreas P. Streich, Mario Frank, David A. Basin, Joachim M. Buhmann:
Multi-assignment clustering for Boolean data. 969-976 - Liang Sun, Shuiwang Ji, Jieping Ye:
A least squares formulation for a class of generalized eigenvalue problems in machine learning. 977-984 - Ilya Sutskever:
A simpler unified analysis of budget perceptrons. 985-992 - 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. 993-1000 - Istvan Szita, András Lörincz:
Optimistic initialization and greediness lead to polynomial time learning in factored MDPs. 1001-1008 - Arthur Szlam, Guillermo Sapiro:
Discriminative k-metrics. 1009-1016 - Gavin Taylor, Ronald Parr:
Kernelized value function approximation for reinforcement learning. 1017-1024 - Graham W. Taylor, Geoffrey E. Hinton:
Factored conditional restricted Boltzmann Machines for modeling motion style. 1025-1032 - Tijmen Tieleman, Geoffrey E. Hinton:
Using fast weights to improve persistent contrastive divergence. 1033-1040 - Robert E. Tillman:
Structure learning with independent non-identically distributed data. 1041-1048 - Marc Toussaint:
Robot trajectory optimization using approximate inference. 1049-1056 - Nicolas Usunier, David Buffoni, Patrick Gallinari:
Ranking with ordered weighted pairwise classification. 1057-1064 - Manik Varma, Bodla Rakesh Babu:
More generality in efficient multiple kernel learning. 1065-1072 - Xuan Vinh Nguyen, Julien Epps, James Bailey:
Information theoretic measures for clusterings comparison: is a correction for chance necessary? 1073-1080 - Nikos Vlassis, Marc Toussaint:
Model-free reinforcement learning as mixture learning. 1081-1088 - Maksims Volkovs, Richard S. Zemel:
BoltzRank: learning to maximize expected ranking gain. 1089-1096 - Kiri L. Wagstaff, Benjamin J. Bornstein:
K-means in space: a radiation sensitivity evaluation. 1097-1104 - Hanna M. Wallach, Iain Murray, Ruslan Salakhutdinov, David M. Mimno:
Evaluation methods for topic models. 1105-1112 - Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, Josh Attenberg:
Feature hashing for large scale multitask learning. 1113-1120 - Max Welling:
Herding dynamical weights to learn. 1121-1128 - Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh:
A stochastic memoizer for sequence data. 1129-1136 - Linli Xu, Martha White, Dale Schuurmans:
Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning. 1137-1144 - Zenglin Xu, Rong Jin, Jieping Ye, Michael R. Lyu, Irwin King:
Non-monotonic feature selection. 1145-1152 - Liu Yang, Rong Jin, Jieping Ye:
Online learning by ellipsoid method. 1153-1160 - Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber:
Stochastic search using the natural gradient. 1161-1168 - Chun-Nam John Yu, Thorsten Joachims:
Learning structural SVMs with latent variables. 1169-1176 - Jia Yuan Yu, Shie Mannor:
Piecewise-stationary bandit problems with side observations. 1177-1184 - Kai Yu, John D. Lafferty, Shenghuo Zhu, Yihong Gong:
Large-scale collaborative prediction using a nonparametric random effects model. 1185-1192 - Xiaotong Yuan, Bao-Gang Hu:
Robust feature extraction via information theoretic learning. 1193-1200 - Yisong Yue, Thorsten Joachims:
Interactively optimizing information retrieval systems as a dueling bandits problem. 1201-1208 - Alan L. Yuille, Songfeng Zheng:
Compositional noisy-logical learning. 1209-1216 - Peng Zang, Peng Zhou, David Minnen, Charles Lee Isbell Jr.:
Discovering options from example trajectories. 1217-1224 - De-Chuan Zhan, Ming Li, Yufeng Li, Zhi-Hua Zhou:
Learning instance specific distances using metric propagation. 1225-1232 - Kai Zhang, James T. Kwok, Bahram Parvin:
Prototype vector machine for large scale semi-supervised learning. 1233-1240 - Wei Zhang, Akshat Surve, Xiaoli Z. Fern, Thomas G. Dietterich:
Learning non-redundant codebooks for classifying complex objects. 1241-1248 - Zhi-Hua Zhou, Yu-Yin Sun, Yufeng Li:
Multi-instance learning by treating instances as non-I.I.D. samples. 1249-1256 - Jun Zhu, Amr Ahmed, Eric P. Xing:
MedLDA: maximum margin supervised topic models for regression and classification. 1257-1264 - Jun Zhu, Eric P. Xing:
On primal and dual sparsity of Markov networks. 1265-1272 - Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi:
SimpleNPKL: simple non-parametric kernel learning. 1273-1280 - Corinna Cortes:
Invited talk: Can learning kernels help performance? 1 - Yoav Freund:
Invited talk: Drifting games, boosting and online learning. 2 - John Mark Agosta, Russell G. Almond, Dennis M. Buede, Marek J. Druzdzel, Judy Goldsmith, Silja Renooij:
Workshop summary: Seventh annual workshop on Bayes applications. 3 - Robert F. Murphy, Chun-Nan Hsu, Loris Nanni:
Workshop summary: Automated interpretation and modelling of cell images. 4 - Kai Yu, Ruslan Salakhutdinov, Yann LeCun, Geoffrey E. Hinton, Yoshua Bengio:
Workshop summary: Workshop on learning feature hierarchies. 5 - David Wingate, Carlos Diuk, Lihong Li, Matthew Taylor, Jordan Frank:
Workshop summary: Results of the 2009 reinforcement learning competition. 6 - Chris Drummond, Nathalie Japkowicz, William Klement, Sofus A. Macskassy:
Workshop summary: The fourth workshop on evaluation methods for machine learning. 7 - Jean-Yves Audibert, Peter Auer, Alessandro Lazaric, Rémi Munos, Daniil Ryabko, Csaba Szepesvári:
Workshop summary: On-line learning with limited feedback. 8 - Matthias W. Seeger, Suvrit Sra, John P. Cunningham:
Workshop summary: Numerical mathematics in machine learning. 9 - Özgür Simsek:
Workshop summary: Abstraction in reinforcement learning. 10 - Douglas Eck, Dan Ellis, Philippe Hamel:
Workshop summary: Sparse methods for music audio. 11 - Alina Beygelzimer, John Langford, Bianca Zadrozny:
Tutorial summary: Reductions in machine learning. 12 - Eyal Even-Dar, Vahab S. Mirrokni:
Tutorial summary: Convergence of natural dynamics to equilibria. 13 - Volker Tresp, Kai Yu:
Tutorial summary: Learning with dependencies between several response variables. 14 - Manfred K. Warmuth, S. V. N. Vishwanathan:
Tutorial summary: Survey of boosting from an optimization perspective. 15 - Yael Niv:
Tutorial summary: The neuroscience of reinforcement learning. 16 - Paul N. Bennett, Misha Bilenko, Kevyn Collins-Thompson:
Tutorial summary: Machine learning in IR: recent successes and new opportunities. 17 - Sanjoy Dasgupta, John Langford:
Tutorial summary: Active learning. 18 - Jure Leskovec:
Tutorial summary: Large social and information networks: opportunities for ML. 19 - Noah A. Smith:
Tutorial summary: Structured prediction for natural language processing. 20
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