Table of Contents

• Gaussian process product models for nonparametric nonstationarityRyan Prescott Adams, Oliver Stegle. 1-8 [doi]
• Sequence kernels for predicting protein essentialityCyril Allauzen, Mehryar Mohri, Ameet Talwalkar. 9-16 [doi]
• Hierarchical kernel stick-breaking process for multi-task image analysisQi An, Chunping Wang, Ivo Shterev, Eric Wang, Lawrence Carin, David B. Dunson. 17-24 [doi]
• Graph kernels between point cloudsFrancis R. Bach. 25-32 [doi]
• Bolasso: model consistent Lasso estimation through the bootstrapFrancis R. Bach. 33-40 [doi]
• Learning all optimal policies with multiple criteriaLeon Barrett, Srini Narayanan. 41-47 [doi]
• Multiple instance rankingCharles Bergeron, Jed Zaretzki, Curt M. Breneman, Kristin P. Bennett. 48-55 [doi]
• Multi-task learning for HIV therapy screeningSteffen Bickel, Jasmina Bogojeska, Thomas Lengauer, Tobias Scheffer. 56-63 [doi]
• Nonnegative matrix factorization via rank-one downdateMichael Biggs, Ali Ghodsi, Stephen A. Vavasis. 64-71 [doi]
• Strategy evaluation in extensive games with importance samplingMichael H. Bowling, Michael Johanson, Neil Burch, Duane Szafron. 72-79 [doi]
• Actively learning level-sets of composite functionsBrent Bryan, Jeff G. Schneider. 80-87 [doi]
• Sparse Bayesian nonparametric regressionFrancois Caron, Arnaud Doucet. 88-95 [doi]
• An empirical evaluation of supervised learning in high dimensionsRich Caruana, Nikolaos Karampatziakis, Ainur Yessenalina. 96-103 [doi]
• Fast support vector machine training and classification on graphics processorsBryan C. Catanzaro, Narayanan Sundaram, Kurt Keutzer. 104-111 [doi]
• Fast nearest neighbor retrieval for bregman divergencesLawrence Cayton. 112-119 [doi]
• Nearest hyperdisk methods for high-dimensional classificationHakan Cevikalp, Bill Triggs, Robi Polikar. 120-127 [doi]
• Learning to sportscast: a test of grounded language acquisitionDavid L. Chen, Raymond J. Mooney. 128-135 [doi]
• Training SVM with indefinite kernelsJianhui Chen, Jieping Ye. 136-143 [doi]
• Learning for control from multiple demonstrationsAdam Coates, Pieter Abbeel, Andrew Y. Ng. 144-151 [doi]
• Spectral clustering with inconsistent adviceTom Coleman, James Saunderson, Anthony Wirth. 152-159 [doi]
• A unified architecture for natural language processing: deep neural networks with multitask learningRonan Collobert, Jason Weston. 160-167 [doi]
• Autonomous geometric precision error estimation in low-level computer vision tasksAndrés Corrada-Emmanuel, Howard J. Schultz. 168-175 [doi]
• Stability of transductive regression algorithmsCorinna Cortes, Mehryar Mohri, Dmitry Pechyony, Ashish Rastogi. 176-183 [doi]
• A rate-distortion one-class model and its applications to clusteringKoby Crammer, Partha Pratim Talukdar, Fernando Pereira. 184-191 [doi]
• Fast Gaussian process methods for point process intensity estimationJohn P. Cunningham, Krishna V. Shenoy, Maneesh Sahani. 192-199 [doi]
• Self-taught clusteringWenyuan Dai, Qiang Yang, Gui-Rong Xue, Yong Yu. 200-207 [doi]
• Hierarchical sampling for active learningSanjoy Dasgupta, Daniel Hsu. 208-215 [doi]
• Learning to classify with missing and corrupted featuresOfer Dekel, Ohad Shamir. 216-223 [doi]
• Maximum likelihood rule ensemblesKrzysztof Dembczynski, Wojciech Kotlowski, Roman Slowinski. 224-231 [doi]
• Learning from incomplete data with infinite imputationsUwe Dick, Peter Haider, Tobias Scheffer. 232-239 [doi]
• An object-oriented representation for efficient reinforcement learningCarlos Diuk, Andre Cohen, Michael L. Littman. 240-247 [doi]
• Optimizing estimated loss reduction for active sampling in rank learningPinar Donmez, Jaime G. Carbonell. 248-255 [doi]
• Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPsFinale Doshi, Joelle Pineau, Nicholas Roy. 256-263 [doi]
• Confidence-weighted linear classificationMark Dredze, Koby Crammer, Fernando Pereira. 264-271 [doi]
• Efficient projections onto the ::::l::::::1::-ball for learning in high dimensionsJohn Duchi, Shai Shalev-Shwartz, Yoram Singer, Tushar Chandra. 272-279 [doi]
• Pointwise exact bootstrap distributions of cost curvesCharles Dugas, David Gadoury. 280-287 [doi]
• Polyhedral classifier for target detection: a case study: colorectal cancerMurat Dundar, Matthias Wolf, Sarang Lakare, Marcos Salganicoff, Vikas C. Raykar. 288-295 [doi]
• Active reinforcement learningArkady Epshteyn, Adam Vogel, Gerald DeJong. 296-303 [doi]
• Training structural SVMs when exact inference is intractableThomas Finley, Thorsten Joachims. 304-311 [doi]
• An HDP-HMM for systems with state persistenceEmily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky. 312-319 [doi]
• Optimized cutting plane algorithm for support vector machinesVojtech Franc, Sören Sonnenburg. 320-327 [doi]
• Stopping conditions for exact computation of leave-one-out error in support vector machinesVojtech Franc, Pavel Laskov, Klaus-Robert Müller. 328-335 [doi]
• Reinforcement learning in the presence of rare eventsJordan Frank, Shie Mannor, Doina Precup. 336-343 [doi]
• Memory bounded inference in topic modelsRyan Gomes, Max Welling, Pietro Perona. 344-351 [doi]
• Localized multiple kernel learningMehmet Gönen, Ethem Alpaydin. 352-359 [doi]
• No-regret learning in convex gamesGeoffrey J. Gordon, Amy R. Greenwald, Casey Marks. 360-367 [doi]
• Boosting with incomplete informationGholamreza Haffari, Yang Wang, Shaojun Wang, Greg Mori, Feng Jiao. 368-375 [doi]
• Grassmann discriminant analysis: a unifying view on subspace-based learningJihun Ham, Daniel D. Lee. 376-383 [doi]
• Modified MMI/MPE: a direct evaluation of the margin in speech recognitionGeorg Heigold, Thomas Deselaers, Ralf Schlüter, Hermann Ney. 384-391 [doi]
• Statistical models for partial membershipKatherine A. Heller, Sinead Williamson, Zoubin Ghahramani. 392-399 [doi]
• Active kernel learningSteven C. H. Hoi, Rong Jin. 400-407 [doi]
• A dual coordinate descent method for large-scale linear SVMCho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Sathiya Keerthi, S. Sundararajan. 408-415 [doi]
• Discriminative structure and parameter learning for Markov logic networksTuyen N. Huynh, Raymond J. Mooney. 416-423 [doi]
• Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-GaussianityAapo Hyvärinen, Shohei Shimizu, Patrik O. Hoyer. 424-431 [doi]
• Efficient bandit algorithms for online multiclass predictionSham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari. 440-447 [doi]
• Large scale manifold transductionMichael Karlen, Jason Weston, Ayse Erkan, Ronan Collobert. 448-455 [doi]
• Non-parametric policy gradients: a unified treatment of propositional and relational domainsKristian Kersting, Kurt Driessens. 456-463 [doi]
• ICA and ISA using Schweizer-Wolff measure of dependenceSergey Kirshner, Barnabás Póczos. 464-471 [doi]
• Unsupervised rank aggregation with distance-based modelsAlexandre Klementiev, Dan Roth, Kevin Small. 472-479 [doi]
• On partial optimality in multi-label MRFsPushmeet Kohli, Alexander Shekhovtsov, Carsten Rother, Vladimir Kolmogorov, Philip H. S. Torr. 480-487 [doi]
• Space-indexed dynamic programming: learning to follow trajectoriesJ. Zico Kolter, Adam Coates, Andrew Y. Ng, Yi Gu, Charles DuHadway. 488-495 [doi]
• The skew spectrum of graphsRisi Imre Kondor, Karsten M. Borgwardt. 496-503 [doi]
• Fast estimation of first-order clause coverage through randomization and maximum likelihoodOndrej Kuzelka, Filip Zelezný. 504-511 [doi]
• Query-level stability and generalization in learning to rankYanyan Lan, Tie-Yan Liu, Tao Qin, Zhiming Ma, Hang Li. 512-519 [doi]
• Modeling interleaved hidden processesNiels Landwehr. 520-527 [doi]
• Exploration scavengingJohn Langford, Alexander L. Strehl, Jennifer Wortman. 528-535 [doi]
• Classification using discriminative restricted Boltzmann machinesHugo Larochelle, Yoshua Bengio. 536-543 [doi]
• Transfer of samples in batch reinforcement learningAlessandro Lazaric, Marcello Restelli, Andrea Bonarini. 544-551 [doi]
• Local likelihood modeling of temporal text streamsGuy Lebanon, Yang Zhao. 552-559 [doi]
• A worst-case comparison between temporal difference and residual gradient with linear function approximationLihong Li. 560-567 [doi]
• Knows what it knows: a framework for self-aware learningLihong Li, Michael L. Littman, Thomas J. Walsh. 568-575 [doi]
• Pairwise constraint propagation by semidefinite programming for semi-supervised classificationZhenguo Li, Jianzhuang Liu, Xiaoou Tang. 576-583 [doi]
• An asymptotic analysis of generative, discriminative, and pseudolikelihood estimatorsPercy Liang, Michael I. Jordan. 584-591 [doi]
• Structure compilation: trading structure for featuresPercy Liang, Hal Daumé III, Dan Klein. 592-599 [doi]
• ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learningNicolas Loeff, David A. Forsyth, Deepak Ramachandran. 600-607 [doi]
• Random classification noise defeats all convex potential boostersPhilip M. Long, Rocco A. Servedio. 608-615 [doi]
• Uncorrelated multilinear principal component analysis through successive variance maximizationHaiping Lu, Konstantinos N. Plataniotis, Anastasios N. Venetsanopoulos. 616-623 [doi]
• A reproducing kernel Hilbert space framework for pairwise time series distancesZhengdong Lu, Todd K. Leen, Yonghong Huang, Deniz Erdogmus. 624-631 [doi]
• On-line discovery of temporal-difference networksTakaki Makino, Toshihisa Takagi. 632-639 [doi]
• Nonextensive entropic kernelsAndré F. T. Martins, Mário A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, Eric P. Xing. 640-647 [doi]
• Automatic discovery and transfer of MAXQ hierarchiesNeville Mehta, Soumya Ray, Prasad Tadepalli, Thomas G. Dietterich. 648-655 [doi]
• Rank minimization via online learningRaghu Meka, Prateek Jain, Constantine Caramanis, Inderjit S. Dhillon. 656-663 [doi]
• An analysis of reinforcement learning with function approximationFrancisco S. Melo, Sean P. Meyn, M. Isabel Ribeiro. 664-671 [doi]
• Empirical Bernstein stoppingVolodymyr Mnih, Csaba Szepesvári, Jean-Yves Audibert. 672-679 [doi]
• Efficiently solving convex relaxations for MAP estimationM. Pawan Kumar, Philip H. S. Torr. 680-687 [doi]
• On the hardness of finding symmetries in Markov decision processesShravan Matthur Narayanamurthy, Balaraman Ravindran. 688-695 [doi]
• Bayes optimal classification for decision treesSiegfried Nijssen. 696-703 [doi]
• A decoupled approach to exemplar-based unsupervised learningSebastian Nowozin, Gökhan H. Bakir. 704-711 [doi]
• Cost-sensitive multi-class classification from probability estimatesDeirdre B. O Brien, Maya R. Gupta, Robert M. Gray. 712-719 [doi]
• The projectron: a bounded kernel-based PerceptronFrancesco Orabona, Joseph Keshet, Barbara Caputo. 720-727 [doi]
• Learning dissimilarities by ranking: from SDP to QPHua Ouyang, Alexander Gray. 728-735 [doi]
• A distance model for rhythmsJean-François Paiement, Yves Grandvalet, Samy Bengio, Douglas Eck. 736-743 [doi]
• On the chance accuracies of large collections of classifiersMark Palatucci, Andrew Carlson. 744-751 [doi]
• An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learningRonald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield, Michael L. Littman. 752-759 [doi]
• Learning to learn implicit queries from gaze patternsKai Puolamäki, Antti Ajanki, Samuel Kaski. 760-767 [doi]
• Multi-task compressive sensing with Dirichlet process priorsYuting Qi, Dehong Liu, David B. Dunson, Lawrence Carin. 768-775 [doi]
• Estimating labels from label proportionsNovi Quadrianto, Alex J. Smola, Tibério S. Caetano, Quoc V. Le. 776-783 [doi]
• Learning diverse rankings with multi-armed banditsFilip Radlinski, Robert Kleinberg, Thorsten Joachims. 784-791 [doi]
• Semi-supervised learning of compact document representations with deep networksMarc Aurelio Ranzato, Martin Szummer. 792-799 [doi]
• Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemesPradeep D. Ravikumar, Alekh Agarwal, Martin J. Wainwright. 800-807 [doi]
• Bayesian multiple instance learning: automatic feature selection and inductive transferVikas C. Raykar, Balaji Krishnapuram, Jinbo Bi, Murat Dundar, R. Bharat Rao. 808-815 [doi]
• Online kernel selection for Bayesian reinforcement learningJoseph Reisinger, Peter Stone, Risto Miikkulainen. 816-823 [doi]
• The dynamic hierarchical Dirichlet processLu Ren, David B. Dunson, Lawrence Carin. 824-831 [doi]
• Closed-form supervised dimensionality reduction with generalized linear modelsIrina Rish, Genady Grabarnik, Guillermo Cecchi, Francisco Pereira, Geoffrey J. Gordon. 832-839 [doi]
• Bi-level path following for cross validated solution of kernel quantile regressionSaharon Rosset. 840-847 [doi]
• The Group-Lasso for generalized linear models: uniqueness of solutions and efficient algorithmsVolker Roth, Bernd Fischer. 848-855 [doi]
• Robust matching and recognition using context-dependent kernelsHichem Sahbi, Jean-Yves Audibert, Jaonary Rabarisoa, Renaud Keriven. 856-863 [doi]
• Privacy-preserving reinforcement learningJun Sakuma, Shigenobu Kobayashi, Rebecca N. Wright. 864-871 [doi]
• On the quantitative analysis of deep belief networksRuslan Salakhutdinov, Iain Murray. 872-879 [doi]
• Bayesian probabilistic matrix factorization using Markov chain Monte CarloRuslan Salakhutdinov, Andriy Mnih. 880-887 [doi]
• Accurate max-margin training for structured output spacesSunita Sarawagi, Rahul Gupta. 888-895 [doi]
• Fast incremental proximity search in large graphsPurnamrita Sarkar, Andrew W. Moore, Amit Prakash. 896-903 [doi]
• Inverting the Viterbi algorithm: an abstract framework for structure designMichael Schnall-Levin, Leonid Chindelevitch, Bonnie Berger. 904-911 [doi]
• Compressed sensing and Bayesian experimental designMatthias W. Seeger, Hannes Nickisch. 912-919 [doi]
• Multi-classification by categorical features via clusteringYevgeny Seldin, Naftali Tishby. 920-927 [doi]
• SVM optimization: inverse dependence on training set sizeShai Shalev-Shwartz, Nathan Srebro. 928-935 [doi]
• Data spectroscopy: learning mixture models using eigenspaces of convolution operatorsTao Shi, Mikhail Belkin, Bin Yu. 936-943 [doi]
• A generalization of Haussler s convolution kernel: mapping kernelKilho Shin, Tetsuji Kuboyama. 944-951 [doi]
• ::::mStruct::::: a new admixture model for inference of population structure in light of both genetic admixing and allele mutationsSuyash Shringarpure, Eric P. Xing. 952-959 [doi]
• Expectation-maximization for sparse and non-negative PCAChristian D. Sigg, Joachim M. Buhmann. 960-967 [doi]
• Sample-based learning and search with permanent and transient memoriesDavid Silver, Richard S. Sutton, Martin Müller 0003. 968-975 [doi]
• An RKHS for multi-view learning and manifold co-regularizationVikas Sindhwani, David S. Rosenberg. 976-983 [doi]
• The asymptotics of semi-supervised learning in discriminative probabilistic modelsNataliya Sokolovska, Olivier Cappé, François Yvon. 984-991 [doi]
• Tailoring density estimation via reproducing kernel moment matchingLe Song, Xinhua Zhang, Alex J. Smola, Arthur Gretton, Bernhard Schölkopf. 992-999 [doi]
• Detecting statistical interactions with additive groves of treesDaria Sorokina, Rich Caruana, Mirek Riedewald, Daniel Fink. 1000-1007 [doi]
• Metric embedding for kernel classification rulesBharath K. Sriperumbudur, Omer A. Lang, Gert R. G. Lanckriet. 1008-1015 [doi]
• Discriminative parameter learning for Bayesian networksJiang Su, Harry Zhang, Charles X. Ling, Stan Matwin. 1016-1023 [doi]
• A least squares formulation for canonical correlation analysisLiang Sun, Shuiwang Ji, Jieping Ye. 1024-1031 [doi]
• Apprenticeship learning using linear programmingUmar Syed, Michael H. Bowling, Robert E. Schapire. 1032-1039 [doi]
• Composite kernel learningMarie Szafranski, Yves Grandvalet, Alain Rakotomamonjy. 1040-1047 [doi]
• The many faces of optimism: a unifying approachIstvan Szita, András Lörincz. 1048-1055 [doi]
• ::::nu::::-support vector machine as conditional value-at-risk minimizationAkiko Takeda, Masashi Sugiyama. 1056-1063 [doi]
• Training restricted Boltzmann machines using approximations to the likelihood gradientTijmen Tieleman. 1064-1071 [doi]
• A semiparametric statistical approach to model-free policy evaluationTsuyoshi Ueno, Motoaki Kawanabe, Takeshi Mori, Shin-ichi Maeda, Shin Ishii. 1072-1079 [doi]
• Topologically-constrained latent variable modelsRaquel Urtasun, David J. Fleet, Andreas Geiger, Jovan Popovic, Trevor Darrell, Neil D. Lawrence. 1080-1087 [doi]
• Beam sampling for the infinite hidden Markov modelJurgen Van Gael, Yunus Saatci, Yee Whye Teh, Zoubin Ghahramani. 1088-1095 [doi]
• Extracting and composing robust features with denoising autoencodersPascal Vincent, Hugo Larochelle, Yoshua Bengio, Pierre-Antoine Manzagol. 1096-1103 [doi]
• Prediction with expert advice for the Brier gameVladimir Vovk, Fedor Zhdanov. 1104-1111 [doi]
• Sparse multiscale gaussian process regressionChristian Walder, Kwang In Kim, Bernhard Schölkopf. 1112-1119 [doi]
• Manifold alignment using Procrustes analysisChang Wang, Sridhar Mahadevan. 1120-1127 [doi]
• Dirichlet component analysis: feature extraction for compositional dataHua-Yan Wang, Qiang Yang, Hong Qin, Hongbin Zha. 1128-1135 [doi]
• Adaptive p-posterior mixture-model kernels for multiple instance learningHua-Yan Wang, Qiang Yang, Hongbin Zha. 1136-1143 [doi]
• Graph transduction via alternating minimizationJun Wang, Tony Jebara, Shih-Fu Chang. 1144-1151 [doi]
• On multi-view active learning and the combination with semi-supervised learningWei Wang, Zhi-Hua Zhou. 1152-1159 [doi]
• Fast solvers and efficient implementations for distance metric learningKilian Q. Weinberger, Lawrence K. Saul. 1160-1167 [doi]
• Deep learning via semi-supervised embeddingJason Weston, Frédéric Ratle, Ronan Collobert. 1168-1175 [doi]
• Efficiently learning linear-linear exponential family predictive representations of stateDavid Wingate, Satinder P. Singh. 1176-1183 [doi]
• Fully distributed EM for very large datasetsJason Wolfe, Aria Haghighi, Dan Klein. 1184-1191 [doi]
• Listwise approach to learning to rank: theory and algorithmFen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, Hang Li. 1192-1199 [doi]
• Democratic approximation of lexicographic preference modelsFusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins. 1200-1207 [doi]
• Preconditioned temporal difference learningHengshuai Yao, Zhi-Qiang Liu. 1208-1215 [doi]
• A quasi-Newton approach to non-smooth convex optimizationJin Yu, S. V. N. Vishwanathan, Simon Günter, Nicol N. Schraudolph. 1216-1223 [doi]
• Predicting diverse subsets using structural SVMsYisong Yue, Thorsten Joachims. 1224-1231 [doi]
• Improved Nyström low-rank approximation and error analysisKai Zhang, Ivor W. Tsang, James T. Kwok. 1232-1239 [doi]
• Estimating local optimums in EM algorithm over Gaussian mixture modelZhenjie Zhang, Bing Tian Dai, Anthony K. H. Tung. 1240-1247 [doi]
• Efficient multiclass maximum margin clusteringBin Zhao, Fei Wang, Changshui Zhang. 1248-1255 [doi]
• Laplace maximum margin Markov networksJun Zhu, Eric P. Xing, Bo Zhang. 1256-1263 [doi]