24. ICML 2007: Corvalis, Oregon, USA

• 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 Krishnaj 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 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 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 P. 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 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 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 Battle, Honglak Lee, Benjamin Packer, Andrew Y. Ng:
  Self-taught learning: transfer learning from unlabeled data. 759-766
  
• Alexander Rakhlin, Jacob Abernethy, Peter L. Bartlett:
  Online discovery of similarity mappings. 767-774
  
• Alain Rakotomamonjy, Francis Bach, Stéphane Canu, Yves Grandvalet:
  More efficiency in multiple kernel learning. 775-782
  
• Matthew J. Rattigan, Marc Maier, David 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, Alex 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 A. 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 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