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ORCID24th ICML 2007: Corvalis, Oregon, USA
- Zoubin Ghahramani:
Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2007), Corvallis, Oregon, USA, June 20-24, 2007. ACM International Conference Proceeding Series 227, ACM 2007, ISBN 978-1-59593-793-3 - Esma Aïmeur, Gilles Brassard, Sébastien Gambs:
Quantum clustering algorithms. 1-8 - Alekh Agarwal, Soumen Chakrabarti:
Learning random walks to rank nodes in graphs. 9-16 - Yonatan Amit, Michael Fink, Nathan Srebro, Shimon Ullman:
Uncovering shared structures in multiclass classification. 17-24 - Rie Kubota Ando, Tong Zhang:
Two-view feature generation model for semi-supervised learning. 25-32 - Galen Andrew, Jianfeng Gao:
Scalable training of L1-regularized log-linear models. 33-40 - S. Asharaf, M. Narasimha Murty, Shirish K. Shevade:
Multiclass core vector machine. 41-48 - Arik Azran:
The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks. 49-56 - Rashmin Babaria, J. Saketha Nath, S. Krishnan, K. R. Sivaramakrishnan, Chiranjib Bhattacharyya, M. Narasimha Murty:
Focused crawling with scalable ordinal regression solvers. 57-64 - Aharon Bar-Hillel, Daphna Weinshall:
Learning distance function by coding similarity. 65-72 - Sourangshu Bhattacharya
, Chiranjib Bhattacharyya, Nagasuma R. Chandra:
Structural alignment based kernels for protein structure classification. 73-80 - Steffen Bickel, Michael Brückner, Tobias Scheffer:
Discriminative learning for differing training and test distributions. 81-88 - Antoine Bordes, Léon Bottou, Patrick Gallinari, Jason Weston:
Solving multiclass support vector machines with LaRank. 89-96 - Brent Bryan, H. Brendan McMahan, Chad M. Schafer, Jeff G. Schneider:
Efficiently computing minimax expected-size confidence regions. 97-104 - Razvan C. Bunescu, Raymond J. Mooney:
Multiple instance learning for sparse positive bags. 105-112 - Ludwig M. Busse, Peter Orbanz, Joachim M. Buhmann:
Cluster analysis of heterogeneous rank data. 113-120 - Bin Cao, Dou Shen, Jian-Tao Sun, Qiang Yang, Zheng Chen:
Feature selection in a kernel space. 121-128 - Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Hang Li:
Learning to rank: from pairwise approach to listwise approach. 129-136 - Luca Cazzanti, Maya R. Gupta:
Local similarity discriminant analysis. 137-144 - Antoni B. Chan, Nuno Vasconcelos, Gert R. G. Lanckriet:
Direct convex relaxations of sparse SVM. 145-153 - Xue-wen Chen, Jong Cheol Jeong:
Minimum reference set based feature selection for small sample classifications. 153-160 - Li Cheng, S. V. N. Vishwanathan:
Learning to compress images and videos. 161-168 - Corinna Cortes, Mehryar Mohri, Ashish Rastogi:
Magnitude-preserving ranking algorithms. 169-176 - Alexandre d'Aspremont, Francis R. Bach, Laurent El Ghaoui:
Full regularization path for sparse principal component analysis. 177-184 - Guang Dai, Dit-Yan Yeung:
Kernel selection forl semi-supervised kernel machines. 185-192 - Wenyuan Dai, Qiang Yang, Gui-Rong Xue, Yong Yu:
Boosting for transfer learning. 193-200 - Ian Davidson, S. S. Ravi:
Intractability and clustering with constraints. 201-208 - Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon:
Information-theoretic metric learning. 209-216 - Jesse Davis, Vítor Santos Costa, Soumya Ray, David Page:
An integrated approach to feature invention and model construction for drug activity prediction. 217-224 - Erick Delage, Shie Mannor:
Percentile optimization in uncertain Markov decision processes with application to efficient exploration. 225-232 - Laura Dietz, Steffen Bickel, Tobias Scheffer:
Unsupervised prediction of citation influences. 233-240 - Piotr Dollár, Vincent C. Rabaud, Serge J. Belongie:
Non-isometric manifold learning: analysis and an algorithm. 241-248 - Miroslav Dudík, David M. Blei, Robert E. Schapire:
Hierarchical maximum entropy density estimation. 249-256 - Roberto Esposito, Daniele Paolo Radicioni:
CarpeDiem: an algorithm for the fast evaluation of SSL classifiers. 257-264 - Amir Massoud Farahmand, Csaba Szepesvári, Jean-Yves Audibert:
Manifold-adaptive dimension estimation. 265-272 - Sylvain Gelly, David Silver:
Combining online and offline knowledge in UCT. 273-280 - Samuel Gerber, Tolga Tasdizen, Ross T. Whitaker:
Robust non-linear dimensionality reduction using successive 1-dimensional Laplacian Eigenmaps. 281-288 - Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc:
Gradient boosting for kernelized output spaces. 289-296 - Mohammad Ghavamzadeh, Yaakov Engel:
Bayesian actor-critic algorithms. 297-304 - Amir Globerson, Terry Koo, Xavier Carreras, Michael Collins:
Exponentiated gradient algorithms for log-linear structured prediction. 305-312 - Nizar Grira, Michael E. Houle:
Best of both: a hybridized centroid-medoid clustering heuristic. 313-320 - Fan Guo, Steve Hanneke, Wenjie Fu, Eric P. Xing:
Recovering temporally rewiring networks: a model-based approach. 321-328 - Rahul Gupta, Ajit A. Diwan, Sunita Sarawagi:
Efficient inference with cardinality-based clique potentials. 329-336 - Romain Hérault, Yves Grandvalet:
Sparse probabilistic classifiers. 337-344 - Peter Haider, Ulf Brefeld, Tobias Scheffer:
Supervised clustering of streaming data for email batch detection. 345-352 - Steve Hanneke:
A bound on the label complexity of agnostic active learning. 353-360 - Steven C. H. Hoi, Rong Jin, Michael R. Lyu:
Learning nonparametric kernel matrices from pairwise constraints. 361-368 - Manfred Jaeger:
Parameter learning for relational Bayesian networks. 369-376 - Shihao Ji, Lawrence Carin:
Bayesian compressive sensing and projection optimization. 377-384 - Jeffrey Johns, Sridhar Mahadevan:
Constructing basis functions from directed graphs for value function approximation. 385-392 - Kristian Kersting, Christian Plagemann, Patrick Pfaff, Wolfram Burgard:
Most likely heteroscedastic Gaussian process regression. 393-400 - Kye-Hyeon Kim, Seungjin Choi:
Neighbor search with global geometry: a minimax message passing algorithm. 401-408 - Minyoung Kim, Vladimir Pavlovic:
A recursive method for discriminative mixture learning. 409-416 - Sergey Kirshner, Padhraic Smyth:
Infinite mixtures of trees. 417-423 - Arto Klami, Samuel Kaski:
Local dependent components. 425-432 - Stanley Kok, Pedro M. Domingos:
Statistical predicate invention. 433-440 - Nicole Krämer, Mikio L. Braun:
Kernelizing PLS, degrees of freedom, and efficient model selection. 441-448 - Andreas Krause, Carlos Guestrin:
Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach. 449-456 - Dmitry Kropotov, Dmitry P. Vetrov:
On one method of non-diagonal regularization in sparse Bayesian learning. 457-464 - Dima Kuzmin, Manfred K. Warmuth:
Online kernel PCA with entropic matrix updates. 465-472 - Hugo Larochelle, Dumitru Erhan, Aaron C. Courville, James Bergstra, Yoshua Bengio:
An empirical evaluation of deep architectures on problems with many factors of variation. 473-480 - Neil D. Lawrence, Andrew J. Moore:
Hierarchical Gaussian process latent variable models. 481-488 - Su-In Lee, Vassil Chatalbashev, David Vickrey, Daphne Koller:
Learning a meta-level prior for feature relevance from multiple related tasks. 489-496 - Jure Leskovec, Christos Faloutsos:
Scalable modeling of real graphs using Kronecker multiplication. 497-504 - Bin Li, Mingmin Chi, Jianping Fan, Xiangyang Xue:
Support cluster machine. 505-512 - Fuxin Li, Jian Yang, Jue Wang:
A transductive framework of distance metric learning by spectral dimensionality reduction. 513-520 - Chris H. Q. Ding, Tao Li:
Adaptive dimension reduction using discriminant analysis and K-means clustering. 521-528 - Wenye Li, Kin-Hong Lee, Kwong-Sak Leung:
Large-scale RLSC learning without agony. 529-536 - Xin Li, William Kwok-Wai Cheung, Jiming Liu, Zhili Wu:
A novel orthogonal NMF-based belief compression for POMDPs. 537-544 - Percy Liang, Michael I. Jordan, Benjamin Taskar:
A permutation-augmented sampler for DP mixture models. 545-552 - Xuejun Liao, Hui Li, Lawrence Carin:
Quadratically gated mixture of experts for incomplete data classification. 553-560 - Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi:
Trust region Newton methods for large-scale logistic regression. 561-568 - Bo Long, Zhongfei (Mark) Zhang, Xiaoyun Wu, Philip S. Yu:
Relational clustering by symmetric convex coding. 569-576 - Yong Ma, Shihong Lao, Erina Takikawa, Masato Kawade:
Discriminant analysis in correlation similarity measure space. 577-584 - Sridhar Mahadevan:
Adaptive mesh compression in 3D computer graphics using multiscale manifold learning. 585-592 - Gideon S. Mann, Andrew McCallum:
Simple, robust, scalable semi-supervised learning via expectation regularization. 593-600 - Bhaskara Marthi:
Automatic shaping and decomposition of reward functions. 601-608 - Hamed Masnadi-Shirazi, Nuno Vasconcelos:
Asymmetric boosting. 609-619 - Graham McNeill, Sethu Vijayakumar:
Linear and nonlinear generative probabilistic class models for shape contours. 617-624 - Lilyana Mihalkova, Raymond J. Mooney:
Bottom-up learning of Markov logic network structure. 625-632 - David M. Mimno, Wei Li, Andrew McCallum:
Mixtures of hierarchical topics with Pachinko allocation. 633-640 - Andriy Mnih, Geoffrey E. Hinton:
Three new graphical models for statistical language modelling. 641-648 - Alessandro Moschitti, Fabio Massimo Zanzotto:
Fast and effective kernels for relational learning from texts. 649-656 - Sofia Mosci, Lorenzo Rosasco, Alessandro Verri:
Dimensionality reduction and generalization. 657-664 - Markos Mylonakis, Khalil Sima'an, Rebecca Hwa:
Unsupervised estimation for noisy-channel models. 665-672 - Blaine Nelson, Ira Cohen:
Revisiting probabilistic models for clustering with pair-wise constraints. 673-680 - Nam Nguyen, Yunsong Guo:
Comparisons of sequence labeling algorithms and extensions. 681-688 - Kai Ni, Lawrence Carin, David B. Dunson:
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process. 689-696 - Jens Nilsson, Fei Sha, Michael I. Jordan:
Regression on manifolds using kernel dimension reduction. 697-704 - Sarah Osentoski, Sridhar Mahadevan:
Learning state-action basis functions for hierarchical MDPs. 705-712 - A. P. Yogananda, M. Narasimha Murty, Lakshmi Gopal:
A fast linear separability test by projection of positive points on subspaces. 713-720 - Sandeep Pandey, Deepayan Chakrabarti, Deepak Agarwal:
Multi-armed bandit problems with dependent arms. 721-728 - Charles Parker, Alan Fern, Prasad Tadepalli:
Learning for efficient retrieval of structured data with noisy queries. 729-736 - Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman:
Analyzing feature generation for value-function approximation. 737-744 - Jan Peters, Stefan Schaal:
Reinforcement learning by reward-weighted regression for operational space control. 745-750 - Chee Wee Phua, Robert Fitch:
Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation. 751-758 - Rajat Raina, Alexis J. Battle, Honglak Lee, Benjamin Packer, Andrew Y. Ng:
Self-taught learning: transfer learning from unlabeled data. 759-766 - Alexander Rakhlin, Jacob D. Abernethy, Peter L. Bartlett:
Online discovery of similarity mappings. 767-774 - Alain Rakotomamonjy, Francis R. Bach, Stéphane Canu, Yves Grandvalet:
More efficiency in multiple kernel learning. 775-782 - Matthew J. Rattigan, Marc E. Maier, David D. Jensen:
Graph clustering with network structure indices. 783-790 - Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hinton:
Restricted Boltzmann machines for collaborative filtering. 791-798 - Mohak Shah:
Sample compression bounds for decision trees. 799-806 - Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro:
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. 807-814 - Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt:
A dependence maximization view of clustering. 815-822 - Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo:
Supervised feature selection via dependence estimation. 823-830 - Bharath K. Sriperumbudur, David A. Torres, Gert R. G. Lanckriet:
Sparse eigen methods by D.C. programming. 831-838 - David H. Stern, Ralf Herbrich, Thore Graepel:
Learning to solve game trees. 839-846 - Jianyong Sun, Ata Kabán, Somak Raychaudhury:
Robust mixtures in the presence of measurement errors. 847-854 - Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf, Kenji Fukumizu:
A kernel-based causal learning algorithm. 855-862 - Charles Sutton, Andrew McCallum:
Piecewise pseudolikelihood for efficient training of conditional random fields. 863-870 - Richard S. Sutton, Anna Koop, David Silver:
On the role of tracking in stationary environments. 871-878 - Matthew E. Taylor, Peter Stone:
Cross-domain transfer for reinforcement learning. 879-886 - Ivan Titov, James Henderson:
Incremental Bayesian networks for structure prediction. 887-894 - Ryota Tomioka, Kazuyuki Aihara:
Classifying matrices with a spectral regularization. 895-902 - Petroula Tsampouka, John Shawe-Taylor:
Approximate maximum margin algorithms with rules controlled by the number of mistakes. 903-910 - Ivor W. Tsang, András Kocsor, James T. Kwok:
Simpler core vector machines with enclosing balls. 911-918 - Koji Tsuda:
Entire regularization paths for graph data. 919-926 - Raquel Urtasun, Trevor Darrell:
Discriminative Gaussian process latent variable model for classification. 927-934 - Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
Experimental perspectives on learning from imbalanced data. 935-942 - Gabriel Wachman, Roni Khardon:
Learning from interpretations: a rooted kernel for ordered hypergraphs. 943-950 - Gang Wang, Dit-Yan Yeung, Frederick H. Lochovsky:
A kernel path algorithm for support vector machines. 951-958 - Hua-Yan Wang, Hongbin Zha, Hong Qin:
Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional data. 959-966 - Huan Wang, Shuicheng Yan, Thomas S. Huang, Jianzhuang Liu, Xiaoou Tang:
Transductive regression piloted by inter-manifold relations. 967-974 - Jack M. Wang, David J. Fleet, Aaron Hertzmann:
Multifactor Gaussian process models for style-content separation. 975-982 - Li Wang, Ji Zhu, Hui Zou:
Hybrid huberized support vector machines for microarray classification. 983-990 - Liwei Wang, Cheng Yang, Jufu Feng:
On learning with dissimilarity functions. 991-998 - Manfred K. Warmuth:
Winnowing subspaces. 999-1006 - Tomás Werner:
What is decreased by the max-sum arc consistency algorithm? 1007-1014 - Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepalli:
Multi-task reinforcement learning: a hierarchical Bayesian approach. 1015-1022 - David P. Wipf, Srikantan S. Nagarajan:
Beamforming using the relevance vector machine. 1023-1030 - Adam Woznica, Alexandros Kalousis, Melanie Hilario:
Learning to combine distances for complex representations. 1031-1038 - Mingrui Wu, Kai Yu, Shipeng Yu, Bernhard Schölkopf:
Local learning projections. 1039-1046 - Yuehua Xu, Alan Fern:
On learning linear ranking functions for beam search. 1047-1054 - Xiang Xuan, Kevin P. Murphy:
Modeling changing dependency structure in multivariate time series. 1055-1062 - Ya Xue, David B. Dunson, Lawrence Carin:
The matrix stick-breaking process for flexible multi-task learning. 1063-1070 - Takehisa Yairi:
Map building without localization by dimensionality reduction techniques. 1071-1078 - Keisuke Yamazaki, Motoaki Kawanabe, Sumio Watanabe, Masashi Sugiyama, Klaus-Robert Müller:
Asymptotic Bayesian generalization error when training and test distributions are different. 1079-1086 - Jieping Ye:
Least squares linear discriminant analysis. 1087-1093 - Jieping Ye, Jianhui Chen, Shuiwang Ji:
Discriminant kernel and regularization parameter learning via semidefinite programming. 1095-1102 - Shipeng Yu, Volker Tresp, Kai Yu:
Robust multi-task learning with t-processes. 1103-1110 - Jian Zhang, Rong Yan:
On the value of pairwise constraints in classification and consistency. 1111-1118 - Kai Zhang, Ivor W. Tsang, James T. Kwok:
Maximum margin clustering made practical. 1119-1126 - Kun Zhang, Laiwan Chan:
Nonlinear independent component analysis with minimal nonlinear distortion. 1127-1134 - Wei Zhang, Xiangyang Xue, Zichen Sun, Yue-Fei Guo, Hong Lu:
Optimal dimensionality of metric space for classification. 1135-1142 - Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanathan:
Conditional random fields for multi-agent reinforcement learning. 1143-1150 - Zheng Zhao, Huan Liu:
Spectral feature selection for supervised and unsupervised learning. 1151-1157 - Dengyong Zhou, Christopher J. C. Burges:
Spectral clustering and transductive learning with multiple views. 1159-1166 - Zhi-Hua Zhou, Jun-Ming Xu:
On the relation between multi-instance learning and semi-supervised learning. 1167-1174 - Jun Zhu, Zaiqing Nie, Bo Zhang, Ji-Rong Wen:
Dynamic hierarchical Markov random fields and their application to web data extraction. 1175-1182 - Alexander Zien, Ulf Brefeld, Tobias Scheffer:
Transductive support vector machines for structured variables. 1183-1190 - Alexander Zien, Cheng Soon Ong:
Multiclass multiple kernel learning. 1191-1198
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