Table of Contents

• The Role of Machine Learning in Business OptimizationChid Apté. 1-2 [doi]
• FAB-MAP: Appearance-Based Place Recognition and Mapping using a Learned Visual Vocabulary ModelMark Joseph Cummins, Paul M. Newman. 3-10 [doi]
• Discriminative Latent Variable Models for Object DetectionPedro F. Felzenszwalb, Ross B. Girshick, David A. McAllester, Deva Ramanan. 11-12 [doi]
• Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft s Bing Search EngineThore Graepel, Joaquin Quiñonero Candela, Thomas Borchert, Ralf Herbrich. 13-20 [doi]
• Music Plus One and Machine LearningChristopher Raphael. 21-28 [doi]
• Climbing the Tower of Babel: Unsupervised Multilingual LearningBenjamin Snyder, Regina Barzilay. 29-36 [doi]
• Detecting Large-Scale System Problems by Mining Console LogsWei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan. 37-46 [doi]
• Particle Filtered MCMC-MLE with Connections to Contrastive DivergenceArthur Asuncion, Qiang Liu, Alexander T. Ihler, Padhraic Smyth. 47-54 [doi]
• Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithmRémi Bardenet, Balázs Kégl. 55-62 [doi]
• Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor ProcessNicholas Bartlett, David Pfau, Frank Wood. 63-70 [doi]
• Robust Formulations for Handling Uncertainty in Kernel MatricesSahely Bhadra, Sourangshu Bhattacharya, Chiranjib Bhattacharyya, Aharon Ben-Tal. 71-78 [doi]
• Active Learning for Networked DataMustafa Bilgic, Lilyana Mihalkova, Lise Getoor. 79-86 [doi]
• Distance dependent Chinese restaurant processesDavid M. Blei, Peter Frazier. 87-94 [doi]
• Causal filter selection in microarray dataGianluca Bontempi, Patrick Emmanuel Meyer. 95-102 [doi]
• Label Ranking under Ambiguous Supervision for Learning Semantic CorrespondencesAntoine Bordes, Nicolas Usunier, Jason Weston. 103-110 [doi]
• A Theoretical Analysis of Feature Pooling in Visual RecognitionY-Lan Boureau, Jean Ponce, Yann LeCun. 111-118 [doi]
• Multi-agent Learning Experiments on Repeated Matrix GamesBruno Bouzy, Marc Métivier. 119-126 [doi]
• Learning Tree Conditional Random FieldsJoseph K. Bradley, Carlos Guestrin. 127-134 [doi]
• Finding Planted Partitions in Nearly Linear Time using Arrested Spectral ClusteringNader H. Bshouty, Philip M. Long. 135-142 [doi]
• Fast boosting using adversarial banditsRóbert Busa-Fekete, Balázs Kégl. 143-150 [doi]
• Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet ProcessKevin R. Canini, Mikhail M. Shashkov, Thomas L. Griffiths. 151-158 [doi]
• Transfer Learning for Collective Link Prediction in Multiple Heterogenous DomainsBin Cao, Nathan Nan Liu, Qiang Yang. 159-166 [doi]
• The Elastic Embedding Algorithm for Dimensionality ReductionMiguel Á. Carreira-Perpiñán. 167-174 [doi]
• Random Spanning Trees and the Prediction of Weighted GraphsNicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella. 175-182 [doi]
• Efficient Learning with Partially Observed AttributesNicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir. 183-190 [doi]
• Convergence, Targeted Optimality, and Safety in Multiagent LearningDoran Chakraborty, Peter Stone. 191-198 [doi]
• Structured Output Learning with Indirect SupervisionMing-Wei Chang, Vivek Srikumar, Dan Goldwasser, Dan Roth. 199-206 [doi]
• Dynamical Products of Experts for Modeling Financial Time SeriesYutian Chen, Max Welling. 207-214 [doi]
• Label Ranking Methods based on the Plackett-Luce ModelWeiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier. 215-222 [doi]
• Graded Multilabel Classification: The Ordinal CaseWeiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier. 223-230 [doi]
• Comparing Clusterings in SpaceMichael H. Coen, M. Hidayath Ansari, Nathanael Fillmore. 231-238 [doi]
• Two-Stage Learning Kernel AlgorithmsCorinna Cortes, Mehryar Mohri, Afshin Rostamizadeh. 239-246 [doi]
• Generalization Bounds for Learning KernelsCorinna Cortes, Mehryar Mohri, Afshin Rostamizadeh. 247-254 [doi]
• Fast Neighborhood Subgraph Pairwise Distance KernelFabrizio Costa, Kurt De Grave. 255-262 [doi]
• Mining Clustering DimensionsSajib Dasgupta, Vincent Ng. 263-270 [doi]
• Bottom-Up Learning of Markov Network StructureJesse Davis, Pedro Domingos. 271-278 [doi]
• Bayes Optimal Multilabel Classification via Probabilistic Classifier ChainsKrzysztof Dembczynski, Weiwei Cheng, Eyke Hüllermeier. 279-286 [doi]
• A Conditional Random Field for Multiple-Instance LearningThomas Deselaers, Vittorio Ferrari. 287-294 [doi]
• Asymptotic Analysis of Generative Semi-Supervised LearningJoshua Dillon, Krishnakumar Balasubramanian, Guy Lebanon. 295-302 [doi]
• Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information SharingFrank Dondelinger, Sophie Lebre, Dirk Husmeier. 303-310 [doi]
• Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting LambdaCarlton Downey, Scott Sanner. 311-318 [doi]
• High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative ModelsGregory Druck, Andrew McCallum. 319-326 [doi]
• On the Consistency of Ranking AlgorithmsJohn Duchi, Lester W. Mackey, Michael I. Jordan. 327-334 [doi]
• Inverse Optimal Control with Linearly-Solvable MDPsDvijotham Krishnamurthy, Emanuel Todorov. 335-342 [doi]
• Continuous-Time Belief PropagationTal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman. 343-350 [doi]
• Nonparametric Information Theoretic Clustering AlgorithmLev Faivishevsky, Jacob Goldberger. 351-358 [doi]
• Feature Selection as a One-Player GameRomaric Gaudel, Michèle Sebag. 359-366 [doi]
• Multiscale Wavelets on Trees, Graphs and High Dimensional Data: Theory and Applications to Semi Supervised LearningMatan Gavish, Boaz Nadler, Ronald R. Coifman. 367-374 [doi]
• A Language-based Approach to Measuring Scholarly ImpactSean Gerrish, David M. Blei. 375-382 [doi]
• Boosting Classifiers with Tightened L0-Relaxation PenaltiesNoam Goldberg, Jonathan Eckstein. 383-390 [doi]
• Budgeted Nonparametric Learning from Data StreamsRyan Gomes, Andreas Krause. 391-398 [doi]
• Learning Fast Approximations of Sparse CodingKarol Gregor, Yann LeCun. 399-406 [doi]
• Boosted Backpropagation Learning for Training Deep Modular NetworksAlexander Grubb, J. Andrew Bagnell. 407-414 [doi]
• Interactive Submodular Set CoverAndrew Guillory, Jeff Bilmes. 415-422 [doi]
• Large Scale Max-Margin Multi-Label Classification with PriorsBharath Hariharan, Lihi Zelnik-Manor, S. V. N. Vishwanathan, Manik Varma. 423-430 [doi]
• Active Learning for Multi-Task Adaptive FilteringAbhay Harpale, Yiming Yang. 431-438 [doi]
• Bayesian Nonparametric Matrix Factorization for Recorded MusicMatthew D. Hoffman, David M. Blei, Perry R. Cook. 439-446 [doi]
• Multi-Task Learning of Gaussian Graphical ModelsJean Honorio, Dimitris Samaras. 447-454 [doi]
• Learning Hierarchical Riffle Independent Groupings from RankingsJonathan Huang, Carlos Guestrin. 455-462 [doi]
• On learning with kernels for unordered pairsMartial Hue, Jean-Philippe Vert. 463-470 [doi]
• A Simple Algorithm for Nuclear Norm Regularized ProblemsMartin Jaggi, Marek Sulovský. 471-478 [doi]
• Telling cause from effect based on high-dimensional observationsDominik Janzing, Patrik O. Hoyer, Bernhard Schölkopf. 479-486 [doi]
• Proximal Methods for Sparse Hierarchical Dictionary LearningRodolphe Jenatton, Julien Mairal, Guillaume Obozinski, Francis Bach. 487-494 [doi]
• 3D Convolutional Neural Networks for Human Action RecognitionShuiwang Ji, Wei Xu, Ming Yang, Kai Yu. 495-502 [doi]
• Accelerated dual decomposition for MAP inferenceVladimir Jojic, Stephen Gould, Daphne Koller. 503-510 [doi]
• Efficient Selection of Multiple Bandit Arms: Theory and PracticeShivaram Kalyanakrishnan, Peter Stone. 511-518 [doi]
• A scalable trust-region algorithm with application to mixed-norm regressionDongmin Kim, Suvrit Sra, Inderjit S. Dhillon. 519-526 [doi]
• Local Minima EmbeddingMinyoung Kim, Fernando De la Torre. 527-534 [doi]
• Gaussian Processes Multiple Instance LearningMinyoung Kim, Fernando De la Torre. 535-542 [doi]
• Tree-Guided Group Lasso for Multi-Task Regression with Structured SparsitySeyoung Kim, Eric P. Xing. 543-550 [doi]
• Learning Markov Logic Networks Using Structural MotifsStanley Kok, Pedro Domingos. 551-558 [doi]
• On Sparse Nonparametric Conditional Covariance SelectionMladen Kolar, Ankur P. Parikh, Eric P. Xing. 559-566 [doi]
• Submodular Dictionary Selection for Sparse RepresentationAndreas Krause, Volkan Cevher. 567-574 [doi]
• Implicit Online LearningBrian Kulis, Peter L. Bartlett. 575-582 [doi]
• Probabilistic Backward and Forward Reasoning in Stochastic Relational WorldsTobias Lang, Marc Toussaint. 583-590 [doi]
• Supervised Aggregation of Classifiers using Artificial Prediction MarketsNathan Lay, Adrian Barbu. 591-598 [doi]
• Bayesian Multi-Task Reinforcement LearningAlessandro Lazaric, Mohammad Ghavamzadeh. 599-606 [doi]
• Analysis of a Classification-based Policy Iteration AlgorithmAlessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos. 607-614 [doi]
• Finite-Sample Analysis of LSTDAlessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos. 615-622 [doi]
• A fast natural Newton methodNicolas Le Roux, Andrew W. Fitzgibbon. 623-630 [doi]
• Making Large-Scale Nyström Approximation PossibleMu Li, James T. Kwok, Bao-Liang Lu. 631-638 [doi]
• Learning Programs: A Hierarchical Bayesian ApproachPercy Liang, Michael I. Jordan, Dan Klein. 639-646 [doi]
• On the Interaction between Norm and Dimensionality: Multiple Regimes in LearningPercy Liang, Nati Srebro. 647-654 [doi]
• Power Iteration ClusteringFrank Lin, William W. Cohen. 655-662 [doi]
• Robust Subspace Segmentation by Low-Rank RepresentationGuangcan Liu, Zhouchen Lin, Yong Yu. 663-670 [doi]
• Robust Graph Mode Seeking by Graph ShiftHairong Liu, Shuicheng Yan. 671-678 [doi]
• Large Graph Construction for Scalable Semi-Supervised LearningWei Liu, Junfeng He, Shih-Fu Chang. 679-686 [doi]
• Learning Temporal Causal Graphs for Relational Time-Series AnalysisYan Liu 0002, Alexandru Niculescu-Mizil, Aurelie C. Lozano, Yong Lu. 687-694 [doi]
• Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial AnalysisDaniel J. Lizotte, Michael H. Bowling, Susan A. Murphy. 695-702 [doi]
• Restricted Boltzmann Machines are Hard to Approximately Evaluate or SimulatePhilip M. Long, Rocco A. Servedio. 703-710 [doi]
• Mixed Membership Matrix FactorizationLester W. Mackey, David Weiss, Michael I. Jordan. 711-718 [doi]
• Toward Off-Policy Learning Control with Function ApproximationHamid Reza Maei, Csaba Szepesvári, Shalabh Bhatnagar, Richard S. Sutton. 719-726 [doi]
• Constructing States for Reinforcement LearningM. M. Hassan Mahmud. 727-734 [doi]
• Deep learning via Hessian-free optimizationJames Martens. 735-742 [doi]
• Learning the Linear Dynamical System with ASOSJames Martens. 743-750 [doi]
• From Transformation-Based Dimensionality Reduction to Feature SelectionMahdokht Masaeli, Glenn Fung, Jennifer G. Dy. 751-758 [doi]
• Risk minimization, probability elicitation, and cost-sensitive SVMsHamed Masnadi-Shirazi, Nuno Vasconcelos. 759-766 [doi]
• Exploiting Data-Independence for Fast Belief-PropagationJulian John McAuley, Tibério S. Caetano. 767-774 [doi]
• Metric Learning to RankBrian McFee, Gert R. G. Lanckriet. 775-782 [doi]
• Learning Efficiently with Approximate Inference via Dual LossesOfer Meshi, David Sontag, Tommi Jaakkola, Amir Globerson. 783-790 [doi]
• Deep Supervised t-Distributed EmbeddingMartin Renqiang Min, Laurens van der Maaten, Zineng Yuan, Anthony J. Bonner, Zhaolei Zhang. 791-798 [doi]
• Nonparametric Return Distribution Approximation for Reinforcement LearningTetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka. 799-806 [doi]
• Rectified Linear Units Improve Restricted Boltzmann MachinesVinod Nair, Geoffrey E. Hinton. 807-814 [doi]
• Implicit Regularization in Variational Bayesian Matrix FactorizationShinichi Nakajima, Masashi Sugiyama. 815-822 [doi]
• Estimation of (near) low-rank matrices with noise and high-dimensional scalingSahand Negahban, Martin J. Wainwright. 823-830 [doi]
• Multiple Non-Redundant Spectral Clustering ViewsDonglin Niu, Jennifer G. Dy, Michael I. Jordan. 831-838 [doi]
• Multiagent Inductive Learning: an Argumentation-based ApproachSantiago Ontañón, Enric Plaza. 839-846 [doi]
• A Stick-Breaking Construction of the Beta ProcessJohn William Paisley, Aimee Zaas, Christopher W. Woods, Geoffrey S. Ginsburg, Lawrence Carin. 847-854 [doi]
• The Margin Perceptron with UnlearningConstantinos Panagiotakopoulos, Petroula Tsampouka. 855-862 [doi]
• Boosting for Regression TransferDavid Pardoe, Peter Stone. 863-870 [doi]
• Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision ProcessesMarek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein. 871-878 [doi]
• Budgeted Distribution Learning of Belief Net ParametersLiuyang Li, Barnabás Póczos, Csaba Szepesvári, Russell Greiner. 879-886 [doi]
• Variable Selection in Model-Based Clustering: To Do or To FacilitateLeonard K. M. Poon, Nevin Lianwen Zhang, Tao Chen, Yi Wang. 887-894 [doi]
• Approximate Predictive Representations of Partially Observable SystemsMonica Dinculescu, Doina Precup. 895-902 [doi]
• Spherical Topic ModelsJoseph Reisinger, Austin Waters, Bryan Silverthorn, Raymond J. Mooney. 903-910 [doi]
• SVM Classifier Estimation from Group ProbabilitiesStefan Rüping. 911-918 [doi]
• Gaussian Process Change Point ModelsYunus Saatci, Ryan Turner, Carl Edward Rasmussen. 927-934 [doi]
• Online Prediction with PrivacyJun Sakuma, Hiromi Arai. 935-942 [doi]
• Learning Deep Boltzmann Machines using Adaptive MCMCRuslan Salakhutdinov. 943-950 [doi]
• Active Risk EstimationChristoph Sawade, Niels Landwehr, Steffen Bickel, Tobias Scheffer. 951-958 [doi]
• Should one compute the Temporal Difference fix point or minimize the Bellman Residual? The unified oblique projection viewBruno Scherrer. 959-966 [doi]
• Gaussian Covariance and Scalable Variational InferenceMatthias W. Seeger. 967-974 [doi]
• Application of Machine Learning To Epileptic Seizure DetectionAli H. Shoeb, John V. Guttag. 975-982 [doi]
• Learning optimally diverse rankings over large document collectionsAleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi. 983-990 [doi]
• Hilbert Space Embeddings of Hidden Markov ModelsLe Song, Sajid M. Siddiqi, Geoffrey J. Gordon, Alexander J. Smola. 991-998 [doi]
• COFFIN: A Computational Framework for Linear SVMsSören Sonnenburg, Vojtech Franc. 999-1006 [doi]
• Internal Rewards Mitigate Agent BoundednessJonathan Sorg, Satinder P. Singh, Richard Lewis. 1007-1014 [doi]
• Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental DesignNiranjan Srinivas, Andreas Krause, Sham Kakade, Matthias Seeger. 1015-1022 [doi]
• Unsupervised Risk Stratification in Clinical Datasets: Identifying Patients at Risk of Rare OutcomesZeeshan Syed, Ilan Rubinfeld. 1023-1030 [doi]
• Model-based reinforcement learning with nearly tight exploration complexity boundsIstvan Szita, Csaba Szepesvári. 1031-1038 [doi]
• Total Variation, Cheeger CutsArthur Szlam, Xavier Bresson. 1039-1046 [doi]
• Learning Sparse SVM for Feature Selection on Very High Dimensional DatasetsMingkui Tan, Li Wang, Ivor W. Tsang. 1047-1054 [doi]
• Deep networks for robust visual recognitionYichuan Tang, Chris Eliasmith. 1055-1062 [doi]
• A DC Programming Approach for Sparse Eigenvalue ProblemMamadou Thiao, Pham Dinh Tao, Le Thi Hoai An. 1063-1070 [doi]
• Least-Squares Policy Iteration: Bias-Variance Trade-off in Control ProblemsChristophe Thiery, Bruno Scherrer. 1071-1078 [doi]
• An Analysis of the Convergence of Graph LaplaciansDaniel Ting, Ling Huang, Michael I. Jordan. 1079-1086 [doi]
• A Fast Augmented Lagrangian Algorithm for Learning Low-Rank MatricesRyota Tomioka, Taiji Suzuki, Masashi Sugiyama, Hisashi Kashima. 1087-1094 [doi]
• One-sided Support Vector Regression for Multiclass Cost-sensitive ClassificationHan-Hsing Tu, Hsuan-Tien Lin. 1095-1102 [doi]
• Non-Local Contrastive ObjectivesDavid Vickrey, Cliff Chiung-Yu Lin, Daphne Koller. 1103-1110 [doi]
• The Translation-invariant Wishart-Dirichlet Process for Clustering Distance DataJulia E. Vogt, Sandhya Prabhakaran, Thomas J. Fuchs, Volker Roth. 1111-1118 [doi]
• Generalizing Apprenticeship Learning across Hypothesis ClassesThomas J. Walsh, Kaushik Subramanian, Michael L. Littman, Carlos Diuk. 1119-1126 [doi]
• Sequential Projection Learning for Hashing with Compact CodesJun Wang, Sanjiv Kumar, Shih-Fu Chang. 1127-1134 [doi]
• A New Analysis of Co-TrainingWei Wang, Zhi-Hua Zhou. 1135-1142 [doi]
• Multi-Class Pegasos on a BudgetZhuang Wang, Koby Crammer, Slobodan Vucetic. 1143-1150 [doi]
• The IBP Compound Dirichlet Process and its Application to Focused Topic ModelingSinead Williamson, Chong Wang, Katherine A. Heller, David M. Blei. 1151-1158 [doi]
• Online Streaming Feature SelectionXindong Wu, Kui Yu, Hao Wang, Wei Ding. 1159-1166 [doi]
• Classes of Multiagent Q-learning Dynamics with epsilon-greedy ExplorationMichael Wunder, Michael L. Littman, Monica Babes. 1167-1174 [doi]
• Simple and Efficient Multiple Kernel Learning by Group LassoZenglin Xu, Rong Jin, Haiqin Yang, Irwin King, Michael R. Lyu. 1175-1182 [doi]
• Sparse Gaussian Process Regression via L1 PenalizationFeng Yan, Yuan (Alan) Qi. 1183-1190 [doi]
• Online Learning for Group LassoHaiqin Yang, Zenglin Xu, Irwin King, Michael R. Lyu. 1191-1198 [doi]
• Learning from Noisy Side Information by Generalized Maximum Entropy ModelTianbao Yang, Rong Jin, Anil K. Jain. 1199-1206 [doi]
• Convergence of Least Squares Temporal Difference Methods Under General ConditionsHuizhen Yu. 1207-1214 [doi]
• Improved Local Coordinate Coding using Local TangentsKai Yu, Tong Zhang. 1215-1222 [doi]
• Projection Penalties: Dimension Reduction without LossYi Zhang 0010, Jeff Schneider. 1223-1230 [doi]
• OTL: A Framework of Online Transfer LearningPeilin Zhao, Steven C. H. Hoi. 1231-1238 [doi]
• Conditional Topic Random FieldsJun Zhu, Eric P. Xing. 1239-1246 [doi]
• Cognitive Models of Test-Item Effects in Human Category LearningXiaojin Zhu, Bryan R. Gibson, Kwang-Sung Jun, Timothy T. Rogers, Joseph Harrison, Chuck Kalish. 1247-1254 [doi]
• Modeling Interaction via the Principle of Maximum Causal EntropyBrian Ziebart, J. Andrew Bagnell, Anind K. Dey. 1255-1262 [doi]