Table of Contents

• Using inaccurate models in reinforcement learningPieter Abbeel, Morgan Quigley, Andrew Y. Ng. 1-8 [doi]
• Algorithms for portfolio management based on the Newton methodAmit Agarwal, Elad Hazan, Satyen Kale, Robert E. Schapire. 9-16 [doi]
• Higher order learning with graphsSameer Agarwal, Kristin Branson, Serge Belongie. 17-24 [doi]
• Ranking on graph dataShivani Agarwal. 25-32 [doi]
• Robust probabilistic projectionsCédric Archambeau, Nicolas Delannay, Michel Verleysen. 33-40 [doi]
• A DC-programming algorithm for kernel selectionAndreas Argyriou, Raphael Hauser, Charles A. Micchelli, Massimiliano Pontil. 41-48 [doi]
• Relational temporal difference learningNima Asgharbeygi, David J. Stracuzzi, Pat Langley. 49-56 [doi]
• A new approach to data driven clusteringArik Azran, Zoubin Ghahramani. 57-64 [doi]
• Agnostic active learningMaria-Florina Balcan, Alina Beygelzimer, John Langford. 65-72 [doi]
• On a theory of learning with similarity functionsMaria-Florina Balcan, Avrim Blum. 73-80 [doi]
• On Bayesian boundsArindam Banerjee. 81-88 [doi]
• Convex optimization techniques for fitting sparse Gaussian graphical modelsOnureena Banerjee, Laurent El Ghaoui, Alexandre d Aspremont, Georges Natsoulis. 89-96 [doi]
• Cover trees for nearest neighborAlina Beygelzimer, Sham Kakade, John Langford. 97-104 [doi]
• Graph model selection using maximum likelihoodIvona Bezáková, Adam Kalai, Rahul Santhanam. 105-112 [doi]
• Dynamic topic modelsDavid M. Blei, John D. Lafferty. 113-120 [doi]
• Predictive search distributionsEdwin V. Bonilla, Christopher K. I. Williams, Felix V. Agakov, John Cavazos, John Thomson, Michael F. P. O Boyle. 121-128 [doi]
• Learning predictive state representations using non-blind policiesMichael H. Bowling, Peter McCracken, Michael James, James Neufeld, Dana F. Wilkinson. 129-136 [doi]
• Efficient co-regularised least squares regressionUlf Brefeld, Thomas Gärtner, Tobias Scheffer, Stefan Wrobel. 137-144 [doi]
• Semi-supervised learning for structured output variablesUlf Brefeld, Tobias Scheffer. 145-152 [doi]
• Fast nonparametric clustering with Gaussian blurring mean-shiftMiguel Á. Carreira-Perpiñán. 153-160 [doi]
• An empirical comparison of supervised learning algorithmsRich Caruana, Alexandru Niculescu-Mizil. 161-168 [doi]
• Robust Euclidean embeddingLawrence Cayton, Sanjoy Dasgupta. 169-176 [doi]
• Hierarchical classification: combining Bayes with SVMNicolò Cesa-Bianchi, Claudio Gentile, Luca Zaniboni. 177-184 [doi]
• A continuation method for semi-supervised SVMsOlivier Chapelle, Mingmin Chi, Alexander Zien. 185-192 [doi]
• A regularization framework for multiple-instance learningPak-Ming Cheung, James T. Kwok. 193-200 [doi]
• Trading convexity for scalabilityRonan Collobert, Fabian H. Sinz, Jason Weston, Léon Bottou. 201-208 [doi]
• Learning algorithms for online principal-agent problems (and selling goods online)Vincent Conitzer, Nikesh Garera. 209-216 [doi]
• Dealing with non-stationary environments using context detectionBruno Castro da Silva, Eduardo W. Basso, Ana L. C. Bazzan, Paulo Martins Engel. 217-224 [doi]
• Locally adaptive classification piloted by uncertaintyJuan Dai, Shuicheng Yan, Xiaoou Tang, James T. Kwok. 225-232 [doi]
• The relationship between Precision-Recall and ROC curvesJesse Davis, Mark Goadrich. 233-240 [doi]
• Discriminative cluster analysisFernando De la Torre, Takeo Kanade. 241-248 [doi]
• Collaborative prediction using ensembles of Maximum Margin Matrix FactorizationsDennis DeCoste. 249-256 [doi]
• Learning the structure of Factored Markov Decision Processes in reinforcement learning problemsThomas Degris, Olivier Sigaud, Pierre-Henri Wuillemin. 257-264 [doi]
• Efficient learning of Naive Bayes classifiers under class-conditional classification noiseFrançois Denis, Christophe Nicolas Magnan, Liva Ralaivola. 265-272 [doi]
• Learning user preferences for sets of objectsMarie desJardins, Eric Eaton, Kiri Wagstaff. 273-280 [doi]
• ::::R::::::1::-PCA: rotational invariant ::::L::::::1::-norm principal component analysis for robust subspace factorizationChris H. Q. Ding, Ding Zhou, Xiaofeng He, Hongyuan Zha. 281-288 [doi]
• Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distributionCharles Elkan. 289-296 [doi]
• A graphical model for predicting protein molecular functionBarbara E. Engelhardt, Michael I. Jordan, Steven E. Brenner. 297-304 [doi]
• Qualitative reinforcement learningArkady Epshteyn, Gerald DeJong. 305-312 [doi]
• Online multiclass learning by interclass hypothesis sharingMichael Fink 0002, Shai Shalev-Shwartz, Yoram Singer, Shimon Ullman. 313-320 [doi]
• Regression with the optimised combination techniqueJochen Garcke. 321-328 [doi]
• A note on mixtures of experts for multiclass responses: approximation rate and Consistent Bayesian InferenceYang Ge, Wenxin Jiang. 329-335 [doi]
• The rate adapting poisson model for information retrieval and object recognitionPeter V. Gehler, Alex Holub, Max Welling. 337-344 [doi]
• Kernelizing the output of tree-based methodsPierre Geurts, Louis Wehenkel, Florence d Alché-Buc. 345-352 [doi]
• Nightmare at test time: robust learning by feature deletionAmir Globerson, Sam T. Roweis. 353-360 [doi]
• A choice model with infinitely many latent featuresDilan Görür, Frank Jäkel, Carl Edward Rasmussen. 361-368 [doi]
• Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networksAlex Graves, Santiago Fernández, Faustino J. Gomez, Jürgen Schmidhuber. 369-376 [doi]
• Practical solutions to the problem of diagonal dominance in kernel document clusteringDerek Greene, Padraig Cunningham. 377-384 [doi]
• Fast transpose methods for kernel learning on sparse dataPatrick Haffner. 385-392 [doi]
• An analysis of graph cut size for transductive learningSteve Hanneke. 393-399 [doi]
• Learning a kernel function for classification with small training samplesTomer Hertz, Aharon Bar-Hillel, Daphna Weinshall. 401-408 [doi]
• Looping suffix tree-based inference of partially observable hidden stateMichael P. Holmes, Charles Lee Isbell Jr.. 409-416 [doi]
• Batch mode active learning and its application to medical image classificationSteven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu. 417-424 [doi]
• Ranking individuals by group comparisonsTzu-Kuo Huang, Chih-Jen Lin, Ruby C. Weng. 425-432 [doi]
• Hidden process modelsRebecca Hutchinson, Tom M. Mitchell, Indrayana Rustandi. 433-440 [doi]
• Estimating relatedness via data compressionBrendan Juba. 441-448 [doi]
• Automatic basis function construction for approximate dynamic programming and reinforcement learningPhilipp W. Keller, Shie Mannor, Doina Precup. 449-456 [doi]
• Personalized handwriting recognition via biased regularizationWolf Kienzle, Kumar Chellapilla. 457-464 [doi]
• Optimal kernel selection in Kernel Fisher discriminant analysisSeung-Jean Kim, Alessandro Magnani, Stephen P. Boyd. 465-472 [doi]
• Pareto optimal linear classificationSeung-Jean Kim, Alessandro Magnani, Sikandar Samar, Stephen P. Boyd, Johan Lim. 473-480 [doi]
• Fast particle smoothing: if I had a million particlesMike Klaas, Mark Briers, Nando de Freitas, Arnaud Doucet, Simon Maskell, Dustin Lang. 481-488 [doi]
• Autonomous shaping: knowledge transfer in reinforcement learningGeorge Konidaris, Andrew G. Barto. 489-496 [doi]
• Data association for topic intensity trackingAndreas Krause, Jure Leskovec, Carlos Guestrin. 497-504 [doi]
• Learning low-rank kernel matricesBrian Kulis, Mátyás Sustik, Inderjit S. Dhillon. 505-512 [doi]
• Local distance preservation in the GP-LVM through back constraintsNeil D. Lawrence, Joaquin Quiñonero Candela. 513-520 [doi]
• Simpler knowledge-based support vector machinesQuoc V. Le, Alex J. Smola, Thomas Gärtner. 521-528 [doi]
• Using query-specific variance estimates to combine Bayesian classifiersChi-Hoon Lee, Russell Greiner, Shaojun Wang. 529-536 [doi]
• A probabilistic model for text kernelsAlain Lehmann, John Shawe-Taylor. 537-544 [doi]
• Efficient MAP approximation for dense energy functionsMarius Leordeanu, Martial Hebert. 545-552 [doi]
• Nonstationary kernel combinationDarrin P. Lewis, Tony Jebara, William Stafford Noble. 553-560 [doi]
• Region-based value iteration for partially observable Markov decision processesHui Li, Xuejun Liao, Lawrence Carin. 561-568 [doi]
• Multiclass boosting with repartitioningLing Li. 569-576 [doi]
• Pachinko allocation: DAG-structured mixture models of topic correlationsWei Li, Andrew McCallum. 577-584 [doi]
• Spectral clustering for multi-type relational dataBo Long, Zhongfei (Mark) Zhang, Xiaoyun Wu, Philip S. Yu. 585-592 [doi]
• Combined central and subspace clustering for computer vision applicationsLe Lu, René Vidal. 593-600 [doi]
• Fast direct policy evaluation using multiscale analysis of Markov diffusion processesMauro Maggioni, Sridhar Mahadevan. 601-608 [doi]
• Pruning in ordered bagging ensemblesGonzalo Martínez-Muñoz, Alberto Suárez. 609-616 [doi]
• Learning high-order MRF priors of color imagesJulian John McAuley, Tibério S. Caetano, Alex J. Smola, Matthias O. Franz. 617-624 [doi]
• The uniqueness of a good optimum for K-meansMarina Meila. 625-632 [doi]
• Kernel information embeddingsRoland Memisevic. 633-640 [doi]
• Generalized spectral bounds for sparse LDABaback Moghaddam, Yair Weiss, Shai Avidan. 641-648 [doi]
• Learning to impersonateMoni Naor, Guy N. Rothblum. 649-656 [doi]
• Online decoding of Markov models under latency constraintsMukund Narasimhan, Paul A. Viola, Michael Shilman. 657-664 [doi]
• Learning hierarchical task networks by observationNegin Nejati, Pat Langley, Tolga Könik. 665-672 [doi]
• Reinforcement learning for optimized trade executionYuriy Nevmyvaka, Yi Feng, Michael S. Kearns. 673-680 [doi]
• Concept boundary detection for speeding up SVMsNavneet Panda, Edward Y. Chang, Gang Wu. 681-688 [doi]
• The support vector decomposition machineFrancisco Pereira, Geoffrey J. Gordon. 689-696 [doi]
• An analytic solution to discrete Bayesian reinforcement learningPascal Poupart, Nikos A. Vlassis, Jesse Hoey, Kevin Regan. 697-704 [doi]
• MISSL: multiple-instance semi-supervised learningRouhollah Rahmani, Sally A. Goldman. 705-712 [doi]
• Constructing informative priors using transfer learningRajat Raina, Andrew Y. Ng, Daphne Koller. 713-720 [doi]
• CN = CPCNLiva Ralaivola, François Denis, Christophe Nicolas Magnan. 721-728 [doi]
• Maximum margin planningNathan D. Ratliff, J. Andrew Bagnell, Martin Zinkevich. 729-736 [doi]
• Quadratic programming relaxations for metric labeling and Markov random field MAP estimationPradeep D. Ravikumar, John D. Lafferty. 737-744 [doi]
• Categorization in multiple category systemsJean-Michel Renders, Éric Gaussier, Cyril Goutte, François Pacull, Gabriela Csurka. 745-752 [doi]
• How boosting the margin can also boost classifier complexityLev Reyzin, Robert E. Schapire. 753-760 [doi]
• Combining discriminative features to infer complex trajectoriesDavid A. Ross, Simon Osindero, Richard S. Zemel. 761-768 [doi]
• Sequential update of ADtreesJosep Roure, Andrew W. Moore. 769-776 [doi]
• Predictive linear-Gaussian models of controlled stochastic dynamical systemsMatthew R. Rudary, Satinder P. Singh. 777-784 [doi]
• A statistical approach to rule learningUlrich Rückert, Stefan Kramer. 785-792 [doi]
• Efficient inference on sequence segmentation modelsSunita Sarawagi. 793-800 [doi]
• Cost-sensitive learning with conditional Markov networksPrithviraj Sen, Lise Getoor. 801-808 [doi]
• Feature value acquisition in testing: a sequential batch test algorithmVictor S. Sheng, Charles X. Ling. 809-816 [doi]
• Permutation invariant SVMsPannagadatta K. Shivaswamy, Tony Jebara. 817-824 [doi]
• Bayesian learning of measurement and structural modelsRicardo Silva, Richard Scheines. 825-832 [doi]
• An intrinsic reward mechanism for efficient explorationÖzgür Simsek, Andrew G. Barto. 833-840 [doi]
• Deterministic annealing for semi-supervised kernel machinesVikas Sindhwani, S. Sathiya Keerthi, Olivier Chapelle. 841-848 [doi]
• Feature subset selection bias for classification learningSurendra K. Singhi, Huan Liu. 849-856 [doi]
• Classifying EEG for brain-computer interfaces: learning optimal filters for dynamical system featuresLe Song, Julien Epps. 857-864 [doi]
• An investigation of computational and informational limits in Gaussian mixture clusteringNathan Srebro, Gregory Shakhnarovich, Sam T. Roweis. 865-872 [doi]
• Bayesian pattern ranking for move prediction in the game of GoDavid H. Stern, Ralf Herbrich, Thore Graepel. 873-880 [doi]
• PAC model-free reinforcement learningAlexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman. 881-888 [doi]
• Experience-efficient learning in associative bandit problemsAlexander L. Strehl, Chris Mesterharm, Michael L. Littman, Haym Hirsh. 889-896 [doi]
• Full Bayesian network classifiersJiang Su, Harry Zhang. 897-904 [doi]
• Local Fisher discriminant analysis for supervised dimensionality reductionMasashi Sugiyama. 905-912 [doi]
• Iterative RELIEF for feature weightingYijun Sun, Jian Li. 913-920 [doi]
• Multiclass reduced-set support vector machinesBenyang Tang, Dominic Mazzoni. 921-928 [doi]
• Fast and space efficient string kernels using suffix arraysChoon Hui Teo, S. V. N. Vishwanathan. 929-936 [doi]
• Bayesian regression with input noise for high dimensional dataJo-Anne Ting, Aaron D Souza, Stefan Schaal. 937-944 [doi]
• Probabilistic inference for solving discrete and continuous state Markov Decision ProcessesMarc Toussaint, Amos J. Storkey. 945-952 [doi]
• Clustering graphs by weighted substructure miningKoji Tsuda, Taku Kudo. 953-960 [doi]
• Active sampling for detecting irrelevant featuresSriharsha Veeramachaneni, Emanuele Olivetti, Paolo Avesani. 961-968 [doi]
• Accelerated training of conditional random fields with stochastic gradient methodsS. V. N. Vishwanathan, Nicol N. Schraudolph, Mark W. Schmidt, Kevin P. Murphy. 969-976 [doi]
• Topic modeling: beyond bag-of-wordsHanna M. Wallach. 977-984 [doi]
• Label propagation through linear neighborhoodsFei Wang, Changshui Zhang. 985-992 [doi]
• Two-dimensional solution path for support vector regressionGang Wang, Dit-Yan Yeung, Frederick H. Lochovsky. 993-1000 [doi]
• Totally corrective boosting algorithms that maximize the marginManfred K. Warmuth, Jun Liao, Gunnar Rätsch. 1001-1008 [doi]
• Inference with the UniversumJason Weston, Ronan Collobert, Fabian H. Sinz, Léon Bottou, Vladimir Vapnik. 1009-1016 [doi]
• Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systemsDavid Wingate, Satinder P. Singh. 1017-1024 [doi]
• Predictive state representations with optionsBritton Wolfe, Satinder P. Singh. 1025-1032 [doi]
• Fast time series classification using numerosity reductionXiaopeng Xi, Eamonn J. Keogh, Christian R. Shelton, Li Wei, Chotirat Ann Ratanamahatana. 1033-1040 [doi]
• A duality view of spectral methods for dimensionality reductionLin Xiao, Jun Sun 0003, Stephen P. Boyd. 1041-1048 [doi]
• Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixtureEric P. Xing, Kyung-Ah Sohn, Michael I. Jordan, Yee Whye Teh. 1049-1056 [doi]
• Discriminative unsupervised learning of structured predictorsLinli Xu, Dana F. Wilkinson, Finnegan Southey, Dale Schuurmans. 1057-1064 [doi]
• Semi-supervised nonlinear dimensionality reductionXin Yang, Haoying Fu, Hongyuan Zha, Jesse L. Barlow. 1065-1072 [doi]
• Null space versus orthogonal linear discriminant analysisJieping Ye, Tao Xiong. 1073-1080 [doi]
• Active learning via transductive experimental designKai Yu, Jinbo Bi, Volker Tresp. 1081-1088 [doi]
• Collaborative ordinal regressionShipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Kriegel. 1089-1096 [doi]
• Block-quantized kernel matrix for fast spectral embeddingKai Zhang, James T. Kwok. 1097-1104 [doi]
• Statistical debugging: simultaneous identification of multiple bugsAlice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, Alex Aiken. 1105-1112 [doi]
• Efficient lazy elimination for averaged one-dependence estimatorsFei Zheng, Geoffrey I. Webb. 1113-1120 [doi]