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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 Miguel 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