Table of Contents

• Quantum clustering algorithmsEsma Aïmeur, Gilles Brassard, Sébastien Gambs. 1-8 [doi]
• Learning random walks to rank nodes in graphsAlekh Agarwal, Soumen Chakrabarti. 9-16 [doi]
• Uncovering shared structures in multiclass classificationYonatan Amit, Michael Fink 0002, Nathan Srebro, Shimon Ullman. 17-24 [doi]
• Two-view feature generation model for semi-supervised learningRie Kubota Ando, Tong Zhang. 25-32 [doi]
• Scalable training of L:::1:::-regularized log-linear modelsGalen Andrew, Jianfeng Gao. 33-40 [doi]
• Multiclass core vector machineS. Asharaf, M. Narasimha Murty, Shirish Krishnaj Shevade. 41-48 [doi]
• The rendezvous algorithm: multiclass semi-supervised learning with Markov random walksArik Azran. 49-56 [doi]
• Focused crawling with scalable ordinal regression solversRashmin Babaria, J. Saketha Nath, S. Krishnan, K. R. Sivaramakrishnan, Chiranjib Bhattacharyya, M. Narasimha Murty. 57-64 [doi]
• Learning distance function by coding similarityAharon Bar-Hillel, Daphna Weinshall. 65-72 [doi]
• Structural alignment based kernels for protein structure classificationSourangshu Bhattacharya, Chiranjib Bhattacharyya, Nagasuma Chandra. 73-80 [doi]
• Discriminative learning for differing training and test distributionsSteffen Bickel, Michael Brückner, Tobias Scheffer. 81-88 [doi]
• Solving multiclass support vector machines with LaRankAntoine Bordes, Léon Bottou, Patrick Gallinari, Jason Weston. 89-96 [doi]
• Efficiently computing minimax expected-size confidence regionsBrent Bryan, H. Brendan McMahan, Chad M. Schafer, Jeff G. Schneider. 97-104 [doi]
• Multiple instance learning for sparse positive bagsRazvan C. Bunescu, Raymond J. Mooney. 105-112 [doi]
• Cluster analysis of heterogeneous rank dataLudwig M. Busse, Peter Orbanz, Joachim M. Buhmann. 113-120 [doi]
• Feature selection in a kernel spaceBin Cao, Dou Shen, Jian-Tao Sun, Qiang Yang, Zheng Chen. 121-128 [doi]
• Learning to rank: from pairwise approach to listwise approachZhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Hang Li. 129-136 [doi]
• Local similarity discriminant analysisLuca Cazzanti, Maya R. Gupta. 137-144 [doi]
• Direct convex relaxations of sparse SVMAntoni B. Chan, Nuno Vasconcelos, Gert R. G. Lanckriet. 145-153 [doi]
• Minimum reference set based feature selection for small sample classificationsXue-wen Chen, Jong Cheol Jeong. 153-160 [doi]
• Learning to compress images and videosLi Cheng, S. V. N. Vishwanathan. 161-168 [doi]
• Magnitude-preserving ranking algorithmsCorinna Cortes, Mehryar Mohri, Ashish Rastogi. 169-176 [doi]
• Full regularization path for sparse principal component analysisAlexandre d Aspremont, Francis R. Bach, Laurent El Ghaoui. 177-184 [doi]
• Kernel selection forl semi-supervised kernel machinesGuang Dai, Dit-Yan Yeung. 185-192 [doi]
• Boosting for transfer learningWenyuan Dai, Qiang Yang, Gui-Rong Xue, Yong Yu. 193-200 [doi]
• Intractability and clustering with constraintsIan Davidson, S. S. Ravi. 201-208 [doi]
• Information-theoretic metric learningJason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon. 209-216 [doi]
• An integrated approach to feature invention and model construction for drug activity predictionJesse Davis, Vítor Santos Costa, Soumya Ray, David Page. 217-224 [doi]
• Percentile optimization in uncertain Markov decision processes with application to efficient explorationErick Delage, Shie Mannor. 225-232 [doi]
• Unsupervised prediction of citation influencesLaura Dietz, Steffen Bickel, Tobias Scheffer. 233-240 [doi]
• Non-isometric manifold learning: analysis and an algorithmPiotr Dollár, Vincent Rabaud, Serge J. Belongie. 241-248 [doi]
• Hierarchical maximum entropy density estimationMiroslav Dudík, David M. Blei, Robert E. Schapire. 249-256 [doi]
• CarpeDiem: an algorithm for the fast evaluation of SSL classifiersRoberto Esposito, Daniele P. Radicioni. 257-264 [doi]
• Manifold-adaptive dimension estimationAmir Massoud Farahmand, Csaba Szepesvári, Jean-Yves Audibert. 265-272 [doi]
• Combining online and offline knowledge in UCTSylvain Gelly, David Silver. 273-280 [doi]
• Robust non-linear dimensionality reduction using successive 1-dimensional Laplacian EigenmapsSamuel Gerber, Tolga Tasdizen, Ross T. Whitaker. 281-288 [doi]
• Gradient boosting for kernelized output spacesPierre Geurts, Louis Wehenkel, Florence d Alché-Buc. 289-296 [doi]
• Bayesian actor-critic algorithmsMohammad Ghavamzadeh, Yaakov Engel. 297-304 [doi]
• Exponentiated gradient algorithms for log-linear structured predictionAmir Globerson, Terry Koo, Xavier Carreras, Michael Collins. 305-312 [doi]
• Best of both: a hybridized centroid-medoid clustering heuristicNizar Grira, Michael E. Houle. 313-320 [doi]
• Recovering temporally rewiring networks: a model-based approachFan Guo, Steve Hanneke, Wenjie Fu, Eric P. Xing. 321-328 [doi]
• Efficient inference with cardinality-based clique potentialsRahul Gupta, Ajit A. Diwan, Sunita Sarawagi. 329-336 [doi]
• Sparse probabilistic classifiersRomain Hérault, Yves Grandvalet. 337-344 [doi]
• Supervised clustering of streaming data for email batch detectionPeter Haider, Ulf Brefeld, Tobias Scheffer. 345-352 [doi]
• A bound on the label complexity of agnostic active learningSteve Hanneke. 353-360 [doi]
• Learning nonparametric kernel matrices from pairwise constraintsSteven C. H. Hoi, Rong Jin, Michael R. Lyu. 361-368 [doi]
• Parameter learning for relational Bayesian networksManfred Jaeger. 369-376 [doi]
• Bayesian compressive sensing and projection optimizationShihao Ji, Lawrence Carin. 377-384 [doi]
• Constructing basis functions from directed graphs for value function approximationJeffrey Johns, Sridhar Mahadevan. 385-392 [doi]
• Most likely heteroscedastic Gaussian process regressionKristian Kersting, Christian Plagemann, Patrick Pfaff, Wolfram Burgard. 393-400 [doi]
• Neighbor search with global geometry: a minimax message passing algorithmKye-Hyeon Kim, Seungjin Choi. 401-408 [doi]
• A recursive method for discriminative mixture learningMinyoung Kim, Vladimir Pavlovic. 409-416 [doi]
• Infinite mixtures of treesSergey Kirshner, Padhraic Smyth. 417-423 [doi]
• Local dependent componentsArto Klami, Samuel Kaski. 425-432 [doi]
• Statistical predicate inventionStanley Kok, Pedro Domingos. 433-440 [doi]
• Kernelizing PLS, degrees of freedom, and efficient model selectionNicole Krämer, Mikio L. Braun. 441-448 [doi]
• Nonmyopic active learning of Gaussian processes: an exploration-exploitation approachAndreas Krause, Carlos Guestrin. 449-456 [doi]
• On one method of non-diagonal regularization in sparse Bayesian learningDmitry Kropotov, Dmitry Vetrov. 457-464 [doi]
• Online kernel PCA with entropic matrix updatesDima Kuzmin, Manfred K. Warmuth. 465-472 [doi]
• An empirical evaluation of deep architectures on problems with many factors of variationHugo Larochelle, Dumitru Erhan, Aaron C. Courville, James Bergstra, Yoshua Bengio. 473-480 [doi]
• Hierarchical Gaussian process latent variable modelsNeil D. Lawrence, Andrew J. Moore. 481-488 [doi]
• Learning a meta-level prior for feature relevance from multiple related tasksSu-In Lee, Vassil Chatalbashev, David Vickrey, Daphne Koller. 489-496 [doi]
• Scalable modeling of real graphs using Kronecker multiplicationJure Leskovec, Christos Faloutsos. 497-504 [doi]
• Support cluster machineBin Li, Mingmin Chi, Jianping Fan, Xiangyang Xue. 505-512 [doi]
• A transductive framework of distance metric learning by spectral dimensionality reductionFuxin Li, Jian Yang, Jue Wang. 513-520 [doi]
• Adaptive dimension reduction using discriminant analysis and ::::K::::-means clusteringChris H. Q. Ding, Tao Li. 521-528 [doi]
• Large-scale RLSC learning without agonyWenye Li, Kin-Hong Lee, Kwong-Sak Leung. 529-536 [doi]
• A novel orthogonal NMF-based belief compression for POMDPsXin Li, William Kwok-Wai Cheung, Jiming Liu, Zhili Wu. 537-544 [doi]
• A permutation-augmented sampler for DP mixture modelsPercy Liang, Michael I. Jordan, Benjamin Taskar. 545-552 [doi]
• Quadratically gated mixture of experts for incomplete data classificationXuejun Liao, Hui Li, Lawrence Carin. 553-560 [doi]
• Trust region Newton methods for large-scale logistic regressionChih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi. 561-568 [doi]
• Relational clustering by symmetric convex codingBo Long, Zhongfei (Mark) Zhang, Xiaoyun Wu, Philip S. Yu. 569-576 [doi]
• Discriminant analysis in correlation similarity measure spaceYong Ma, Shihong Lao, Erina Takikawa, Masato Kawade. 577-584 [doi]
• Adaptive mesh compression in 3D computer graphics using multiscale manifold learningSridhar Mahadevan. 585-592 [doi]
• Simple, robust, scalable semi-supervised learning via expectation regularizationGideon S. Mann, Andrew McCallum. 593-600 [doi]
• Automatic shaping and decomposition of reward functionsBhaskara Marthi. 601-608 [doi]
• Asymmetric boostingHamed Masnadi-Shirazi, Nuno Vasconcelos. 609-619 [doi]
• Linear and nonlinear generative probabilistic class models for shape contoursGraham McNeill, Sethu Vijayakumar. 617-624 [doi]
• Bottom-up learning of Markov logic network structureLilyana Mihalkova, Raymond J. Mooney. 625-632 [doi]
• Mixtures of hierarchical topics with Pachinko allocationDavid M. Mimno, Wei Li, Andrew McCallum. 633-640 [doi]
• Three new graphical models for statistical language modellingAndriy Mnih, Geoffrey E. Hinton. 641-648 [doi]
• Fast and effective kernels for relational learning from textsAlessandro Moschitti, Fabio Massimo Zanzotto. 649-656 [doi]
• Dimensionality reduction and generalizationSofia Mosci, Lorenzo Rosasco, Alessandro Verri. 657-664 [doi]
• Unsupervised estimation for noisy-channel modelsMarkos Mylonakis, Khalil Sima an, Rebecca Hwa. 665-672 [doi]
• Revisiting probabilistic models for clustering with pair-wise constraintsBlaine Nelson, Ira Cohen. 673-680 [doi]
• Comparisons of sequence labeling algorithms and extensionsNam Nguyen, Yunsong Guo. 681-688 [doi]
• Multi-task learning for sequential data via iHMMs and the nested Dirichlet processKai Ni, Lawrence Carin, David B. Dunson. 689-696 [doi]
• Regression on manifolds using kernel dimension reductionJens Nilsson, Fei Sha, Michael I. Jordan. 697-704 [doi]
• Learning state-action basis functions for hierarchical MDPsSarah Osentoski, Sridhar Mahadevan. 705-712 [doi]
• A fast linear separability test by projection of positive points on subspacesA. P. Yogananda, M. Narasimha Murty, Lakshmi Gopal. 713-720 [doi]
• Multi-armed bandit problems with dependent armsSandeep Pandey, Deepayan Chakrabarti, Deepak Agarwal. 721-728 [doi]
• Learning for efficient retrieval of structured data with noisy queriesCharles Parker, Alan Fern, Prasad Tadepalli. 729-736 [doi]
• Analyzing feature generation for value-function approximationRonald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman. 737-744 [doi]
• Reinforcement learning by reward-weighted regression for operational space controlJan Peters, Stefan Schaal. 745-750 [doi]
• Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximationChee Wee Phua, Robert Fitch. 751-758 [doi]
• Self-taught learning: transfer learning from unlabeled dataRajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, Andrew Y. Ng. 759-766 [doi]
• Online discovery of similarity mappingsAlexander Rakhlin, Jacob Abernethy, Peter L. Bartlett. 767-774 [doi]
• More efficiency in multiple kernel learningAlain Rakotomamonjy, Francis Bach, Stéphane Canu, Yves Grandvalet. 775-782 [doi]
• Graph clustering with network structure indicesMatthew J. Rattigan, Marc Maier, David Jensen. 783-790 [doi]
• Restricted Boltzmann machines for collaborative filteringRuslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hinton. 791-798 [doi]
• Sample compression bounds for decision treesMohak Shah. 799-806 [doi]
• Pegasos: Primal Estimated sub-GrAdient SOlver for SVMShai Shalev-Shwartz, Yoram Singer, Nathan Srebro. 807-814 [doi]
• A dependence maximization view of clusteringLe Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt. 815-822 [doi]
• Supervised feature selection via dependence estimationLe Song, Alex J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo. 823-830 [doi]
• Sparse eigen methods by D.C. programmingBharath K. Sriperumbudur, David A. Torres, Gert R. G. Lanckriet. 831-838 [doi]
• Learning to solve game treesDavid H. Stern, Ralf Herbrich, Thore Graepel. 839-846 [doi]
• Robust mixtures in the presence of measurement errorsJianyong Sun, Ata Kabán, Somak Raychaudhury. 847-854 [doi]
• A kernel-based causal learning algorithmXiaohai Sun, Dominik Janzing, Bernhard Schölkopf, Kenji Fukumizu. 855-862 [doi]
• Piecewise pseudolikelihood for efficient training of conditional random fieldsCharles A. Sutton, Andrew McCallum. 863-870 [doi]
• On the role of tracking in stationary environmentsRichard S. Sutton, Anna Koop, David Silver. 871-878 [doi]
• Cross-domain transfer for reinforcement learningMatthew E. Taylor, Peter Stone. 879-886 [doi]
• Incremental Bayesian networks for structure predictionIvan Titov, James Henderson. 887-894 [doi]
• Classifying matrices with a spectral regularizationRyota Tomioka, Kazuyuki Aihara. 895-902 [doi]
• Approximate maximum margin algorithms with rules controlled by the number of mistakesPetroula Tsampouka, John Shawe-Taylor. 903-910 [doi]
• Simpler core vector machines with enclosing ballsIvor W. Tsang, András Kocsor, James T. Kwok. 911-918 [doi]
• Entire regularization paths for graph dataKoji Tsuda. 919-926 [doi]
• Discriminative Gaussian process latent variable model for classificationRaquel Urtasun, Trevor Darrell. 927-934 [doi]
• Experimental perspectives on learning from imbalanced dataJason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano. 935-942 [doi]
• Learning from interpretations: a rooted kernel for ordered hypergraphsGabriel Wachman, Roni Khardon. 943-950 [doi]
• A kernel path algorithm for support vector machinesGang Wang, Dit-Yan Yeung, Frederick H. Lochovsky. 951-958 [doi]
• Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional dataHua-Yan Wang, Hongbin Zha, Hong Qin. 959-966 [doi]
• Transductive regression piloted by inter-manifold relationsHuan Wang, Shuicheng Yan, Thomas S. Huang, Jianzhuang Liu, Xiaoou Tang. 967-974 [doi]
• Multifactor Gaussian process models for style-content separationJack M. Wang, David J. Fleet, Aaron Hertzmann. 975-982 [doi]
• Hybrid huberized support vector machines for microarray classificationLi Wang, Ji Zhu, Hui Zou. 983-990 [doi]
• On learning with dissimilarity functionsLiwei Wang, Cheng Yang, Jufu Feng. 991-998 [doi]
• Winnowing subspacesManfred K. Warmuth. 999-1006 [doi]
• What is decreased by the max-sum arc consistency algorithm?Tomás Werner. 1007-1014 [doi]
• Multi-task reinforcement learning: a hierarchical Bayesian approachAaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepalli. 1015-1022 [doi]
• Beamforming using the relevance vector machineDavid P. Wipf, Srikantan Nagarajan. 1023-1030 [doi]
• Learning to combine distances for complex representationsAdam Woznica, Alexandros Kalousis, Melanie Hilario. 1031-1038 [doi]
• Local learning projectionsMingrui Wu, Kai Yu, Shipeng Yu, Bernhard Schölkopf. 1039-1046 [doi]
• On learning linear ranking functions for beam searchYuehua Xu, Alan Fern. 1047-1054 [doi]
• Modeling changing dependency structure in multivariate time seriesXiang Xuan, Kevin P. Murphy. 1055-1062 [doi]
• The matrix stick-breaking process for flexible multi-task learningYa Xue, David B. Dunson, Lawrence Carin. 1063-1070 [doi]
• Map building without localization by dimensionality reduction techniquesTakehisa Yairi. 1071-1078 [doi]
• Asymptotic Bayesian generalization error when training and test distributions are differentKeisuke Yamazaki, Motoaki Kawanabe, Sumio Watanabe, Masashi Sugiyama, Klaus-Robert Müller. 1079-1086 [doi]
• Least squares linear discriminant analysisJieping Ye. 1087-1093 [doi]
• Discriminant kernel and regularization parameter learning via semidefinite programmingJieping Ye, Jianhui Chen, Shuiwang Ji. 1095-1102 [doi]
• Robust multi-task learning with ::::t::::-processesShipeng Yu, Volker Tresp, Kai Yu. 1103-1110 [doi]
• On the value of pairwise constraints in classification and consistencyJian Zhang, Rong Yan. 1111-1118 [doi]
• Maximum margin clustering made practicalKai Zhang, Ivor W. Tsang, James T. Kwok. 1119-1126 [doi]
• Nonlinear independent component analysis with minimal nonlinear distortionKun Zhang, Laiwan Chan. 1127-1134 [doi]
• Optimal dimensionality of metric space for classificationWei Zhang, Xiangyang Xue, Zichen Sun, Yue-Fei Guo, Hong Lu. 1135-1142 [doi]
• Conditional random fields for multi-agent reinforcement learningXinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanathan. 1143-1150 [doi]
• Spectral feature selection for supervised and unsupervised learningZheng Zhao, Huan Liu. 1151-1157 [doi]
• Spectral clustering and transductive learning with multiple viewsDengyong Zhou, Christopher J. C. Burges. 1159-1166 [doi]
• On the relation between multi-instance learning and semi-supervised learningZhi-Hua Zhou, Jun Ming Xu. 1167-1174 [doi]
• Dynamic hierarchical Markov random fields and their application to web data extractionJun Zhu, Zaiqing Nie, Bo Zhang, Ji-Rong Wen. 1175-1182 [doi]
• Transductive support vector machines for structured variablesAlexander Zien, Ulf Brefeld, Tobias Scheffer. 1183-1190 [doi]
• Multiclass multiple kernel learningAlexander Zien, Cheng Soon Ong. 1191-1198 [doi]