Abstract is missing.
- Archipelago: nonparametric Bayesian semi-supervised learningRyan Prescott Adams, Zoubin Ghahramani. 1 [doi]
- Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensitiesRyan Prescott Adams, Iain Murray, David J. C. MacKay. 2 [doi]
- Route kernels for treesFabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti. 3 [doi]
- Incorporating domain knowledge into topic modeling via Dirichlet Forest priorsDavid Andrzejewski, Xiaojin Zhu, Mark Craven. 4 [doi]
- Grammatical inference as a principal component analysis problemRaphaël Bailly, François Denis, Liva Ralaivola. 5 [doi]
- Curriculum learningYoshua Bengio, Jérôme Louradour, Ronan Collobert, Jason Weston. 6 [doi]
- Importance weighted active learningAlina Beygelzimer, Sanjoy Dasgupta, John Langford. 7 [doi]
- Split variational inferenceGuillaume Bouchard, Onno Zoeter. 8 [doi]
- Predictive representations for policy gradient in POMDPsAbdeslam Boularias, Brahim Chaib-draa. 9 [doi]
- Online feature elicitation in interactive optimizationCraig Boutilier, Kevin Regan, Paolo Viappiani. 10 [doi]
- Spectral clustering based on the graph ::::p::::-LaplacianThomas Bühler, Matthias Hein. 11 [doi]
- Active learning for directed exploration of complex systemsMichael C. Burl, Esther Wang. 12 [doi]
- Optimized expected information gain for nonlinear dynamical systemsAlberto Giovanni Busetto, Cheng Soon Ong, Joachim M. Buhmann. 13 [doi]
- Probabilistic dyadic data analysis with local and global consistencyDeng Cai, Xuanhui Wang, Xiaofei He. 14 [doi]
- Structure learning of Bayesian networks using constraintsCassio Polpo de Campos, Zhi Zeng, Qiang Ji. 15 [doi]
- Robust bounds for classification via selective samplingNicolò Cesa-Bianchi, Claudio Gentile, Francesco Orabona. 16 [doi]
- Multi-view clustering via canonical correlation analysisKamalika Chaudhuri, Sham M. Kakade, Karen Livescu, Karthik Sridharan. 17 [doi]
- A convex formulation for learning shared structures from multiple tasksJianhui Chen, Lei Tang, Jun Liu, Jieping Ye. 18 [doi]
- Learning kernels from indefinite similaritiesYihua Chen, Maya R. Gupta, Benjamin Recht. 19 [doi]
- Matrix updates for perceptron training of continuous density hidden Markov modelsChih-Chieh Cheng, Fei Sha, Lawrence K. Saul. 20 [doi]
- Decision tree and instance-based learning for label rankingWeiwei Cheng, Jens C. Huhn, Eyke Hüllermeier. 21 [doi]
- Learning dictionaries of stable autoregressive models for audio scene analysisYoungmin Cho, Lawrence K. Saul. 22 [doi]
- Exploiting sparse Markov ::::and:::: covariance structure in multiresolution modelsMyung Jin Choi, Venkat Chandrasekaran, Alan S. Willsky. 23 [doi]
- Nonparametric estimation of the precision-recall curveStéphan Clémençon, Nicolas Vayatis. 24 [doi]
- EigenTransfer: a unified framework for transfer learningWenyuan Dai, Ou Jin, Gui-Rong Xue, Qiang Yang, Yong Yu. 25 [doi]
- Fitting a graph to vector dataSamuel I. Daitch, Jonathan A. Kelner, Daniel A. Spielman. 26 [doi]
- Unsupervised search-based structured predictionHal Daumé III. 27 [doi]
- Deep transfer via second-order Markov logicJesse Davis, Pedro Domingos. 28 [doi]
- Analytic moment-based Gaussian process filteringMarc Peter Deisenroth, Marco F. Huber, Uwe D. Hanebeck. 29 [doi]
- Good learners for evil teachersOfer Dekel, Ohad Shamir. 30 [doi]
- A scalable framework for discovering coherent co-clusters in noisy dataMeghana Deodhar, Gunjan Gupta, Joydeep Ghosh, Hyuk Cho, Inderjit S. Dhillon. 31 [doi]
- The adaptive ::::k::::-meteorologists problem and its application to structure learning and feature selection in reinforcement learningCarlos Diuk, Lihong Li, Bethany R. Leffler. 32 [doi]
- Proximal regularization for online and batch learningChuong B. Do, Quoc V. Le, Chuan-Sheng Foo. 33 [doi]
- Large margin training for hidden Markov models with partially observed statesTrinh Minh Tri Do, Thierry Artières. 34 [doi]
- Accelerated sampling for the Indian Buffet ProcessFinale Doshi-Velez, Zoubin Ghahramani. 35 [doi]
- Accounting for burstiness in topic modelsGabriel Doyle, Charles Elkan. 36 [doi]
- Domain adaptation from multiple sources via auxiliary classifiersLixin Duan, Ivor W. Tsang, Dong Xu, Tat-Seng Chua. 37 [doi]
- Boosting with structural sparsityJohn Duchi, Yoram Singer. 38 [doi]
- Learning to segment from a few well-selected training imagesAlireza Farhangfar, Russell Greiner, Csaba Szepesvári. 39 [doi]
- GAODE and HAODE: two proposals based on AODE to deal with continuous variablesM. Julia Flores, José A. Gámez, Ana M. Martínez, Jose Miguel Puerta. 40 [doi]
- A majorization-minimization algorithm for (multiple) hyperparameter learningChuan-Sheng Foo, Chuong B. Do, Andrew Y. Ng. 41 [doi]
- Dynamic mixed membership blockmodel for evolving networksWenjie Fu, Le Song, Eric P. Xing. 42 [doi]
- Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry propertyRahul Garg, Rohit Khandekar. 43 [doi]
- Sequential Bayesian prediction in the presence of changepointsRoman Garnett, Michael A. Osborne, Stephen J. Roberts. 44 [doi]
- PAC-Bayesian learning of linear classifiersPascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand. 45 [doi]
- Fast evolutionary maximum margin clusteringFabian Gieseke, Tapio Pahikkala, Oliver Kramer. 46 [doi]
- Dynamic analysis of multiagent ::::Q::::-learning with ε-greedy explorationEduardo Rodrigues Gomes, Ryszard Kowalczyk. 47 [doi]
- Bayesian inference for Plackett-Luce ranking modelsJohn Guiver, Edward Snelson. 48 [doi]
- Bayesian clustering for email campaign detectionPeter Haider, Tobias Scheffer. 49 [doi]
- Efficient learning algorithms for changing environmentsElad Hazan, C. Seshadhri. 50 [doi]
- Hoeffding and Bernstein races for selecting policies in evolutionary direct policy searchVerena Heidrich-Meisner, Christian Igel. 51 [doi]
- Partially supervised feature selection with regularized linear modelsThibault Helleputte, Pierre Dupont. 52 [doi]
- Learning with structured sparsityJunZhou Huang, Tong Zhang, Dimitris N. Metaxas. 53 [doi]
- Learning linear dynamical systems without sequence informationTzu-Kuo Huang, Jeff Schneider. 54 [doi]
- Group lasso with overlap and graph lassoLaurent Jacob, Guillaume Obozinski, Jean-Philippe Vert. 55 [doi]
- Graph construction and ::::b::::-matching for semi-supervised learningTony Jebara, Jun Wang, Shih-Fu Chang. 56 [doi]
- Trajectory prediction: learning to map situations to robot trajectoriesNikolay Jetchev, Marc Toussaint. 57 [doi]
- An accelerated gradient method for trace norm minimizationShuiwang Ji, Jieping Ye. 58 [doi]
- A novel lexicalized HMM-based learning framework for web opinion miningWei Jin, Hung Hay Ho. 59 [doi]
- Orbit-product representation and correction of Gaussian belief propagationJason K. Johnson, Vladimir Y. Chernyak, Michael Chertkov. 60 [doi]
- A Bayesian approach to protein model quality assessmentHetunandan Kamisetty, Christopher James Langmead. 61 [doi]
- Learning prediction suffix trees with WinnowNikolaos Karampatziakis, Dexter Kozen. 62 [doi]
- Boosting products of base classifiersBalázs Kégl, Róbert Busa-Fekete. 63 [doi]
- Learning Markov logic network structure via hypergraph liftingStanley Kok, Pedro Domingos. 64 [doi]
- Near-Bayesian exploration in polynomial timeJ. Zico Kolter, Andrew Y. Ng. 65 [doi]
- Regularization and feature selection in least-squares temporal difference learningJ. Zico Kolter, Andrew Y. Ng. 66 [doi]
- The graphlet spectrumRisi Imre Kondor, Nino Shervashidze, Karsten M. Borgwardt. 67 [doi]
- Rule learning with monotonicity constraintsWojciech Kotlowski, Roman Slowinski. 68 [doi]
- Multiple indefinite kernel learning with mixed norm regularizationMatthieu Kowalski, Marie Szafranski, Liva Ralaivola. 69 [doi]
- On sampling-based approximate spectral decompositionSanjiv Kumar, Mehryar Mohri, Ameet Talwalkar. 70 [doi]
- Learning spectral graph transformations for link predictionJérôme Kunegis, Andreas Lommatzsch. 71 [doi]
- Block-wise construction of acyclic relational features with monotone irreducibility and relevancy propertiesOndrej Kuzelka, Filip Zelezný. 72 [doi]
- Generalization analysis of listwise learning-to-rank algorithmsYanyan Lan, Tie-Yan Liu, Zhiming Ma, Hang Li. 73 [doi]
- Approximate inference for planning in stochastic relational worldsTobias Lang, Marc Toussaint. 74 [doi]
- Learning nonlinear dynamic modelsJohn Langford, Ruslan Salakhutdinov, Tong Zhang. 75 [doi]
- Non-linear matrix factorization with Gaussian processesNeil D. Lawrence, Raquel Urtasun. 76 [doi]
- Convolutional deep belief networks for scalable unsupervised learning of hierarchical representationsHonglak Lee, Roger Grosse, Rajesh Ranganath, Andrew Y. Ng. 77 [doi]
- Transfer learning for collaborative filtering via a rating-matrix generative modelBin Li, Qiang Yang, Xiangyang Xue. 78 [doi]
- ABC-boost: adaptive base class boost for multi-class classificationPing Li. 79 [doi]
- Semi-supervised learning using label meanYu-Feng Li, James T. Kwok, Zhi-Hua Zhou. 80 [doi]
- Learning from measurements in exponential familiesPercy Liang, Michael I. Jordan, Dan Klein. 81 [doi]
- Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discoveryHan Liu, Mark Palatucci, Jian Zhang. 82 [doi]
- Efficient Euclidean projections in linear timeJun Liu, Jieping Ye. 83 [doi]
- Topic-link LDA: joint models of topic and author communityYan Liu, Alexandru Niculescu-Mizil, Wojciech Gryc. 84 [doi]
- Geometry-aware metric learningZhengdong Lu, Prateek Jain, Inderjit S. Dhillon. 85 [doi]
- Identifying suspicious URLs: an application of large-scale online learningJustin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker. 86 [doi]
- Online dictionary learning for sparse codingJulien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro. 87 [doi]
- Proto-predictive representation of states with simple recurrent temporal-difference networksTakaki Makino. 88 [doi]
- Sparse Gaussian graphical models with unknown block structureBenjamin M. Marlin, Kevin P. Murphy. 89 [doi]
- Polyhedral outer approximations with application to natural language parsingAndré F. T. Martins, Noah A. Smith, Eric P. Xing. 90 [doi]
- Partial order embedding with multiple kernelsBrian McFee, Gert R. G. Lanckriet. 91 [doi]
- Bandit-based optimization on graphs with application to library performance tuningFrédéric de Mesmay, Arpad Rimmel, Yevgen Voronenko, Markus Püschel. 92 [doi]
- Deep learning from temporal coherence in videoHossein Mobahi, Ronan Collobert, Jason Weston. 93 [doi]
- Regression by dependence minimization and its application to causal inference in additive noise modelsJoris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf. 94 [doi]
- Learning complex motions by sequencing simpler motion templatesGerhard Neumann, Wolfgang Maass, Jan Peters. 95 [doi]
- Convex variational Bayesian inference for large scale generalized linear modelsHannes Nickisch, Matthias W. Seeger. 96 [doi]
- Solution stability in linear programming relaxations: graph partitioning and unsupervised learningSebastian Nowozin, Stefanie Jegelka. 97 [doi]
- Nonparametric factor analysis with beta process priorsJohn William Paisley, Lawrence Carin. 98 [doi]
- Unsupervised hierarchical modeling of locomotion stylesWei Pan, Lorenzo Torresani. 99 [doi]
- Binary action search for learning continuous-action control policiesJason Pazis, Michail G. Lagoudakis. 100 [doi]
- Detecting the direction of causal time seriesJonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf. 101 [doi]
- Constraint relaxation in approximate linear programsMarek Petrik, Shlomo Zilberstein. 102 [doi]
- Multi-class image segmentation using conditional random fields and global classificationNils Plath, Marc Toussaint, Shinichi Nakajima. 103 [doi]
- Learning when to stop thinking and do something!Barnabás Póczos, Yasin Abbasi-Yadkori, Csaba Szepesvári, Russell Greiner, Nathan R. Sturtevant. 104 [doi]
- Independent factor topic modelsDuangmanee Putthividhya, Hagai Thomas Attias, Srikantan S. Nagarajan. 105 [doi]
- An efficient sparse metric learning in high-dimensional space via ::::l::::::1::-penalized log-determinant regularizationGuo-Jun Qi, Jinhui Tang, Zheng-Jun Zha, Tat-Seng Chua, Hong-Jiang Zhang. 106 [doi]
- Sparse higher order conditional random fields for improved sequence labelingXian Qian, Xiaoqian Jiang, Qi Zhang, Xuanjing Huang, Lide Wu. 107 [doi]
- An efficient projection for ::::l::::::1::, ::∞:: regularizationAriadna Quattoni, Xavier Carreras, Michael Collins, Trevor Darrell. 108 [doi]
- Nearest neighbors in high-dimensional data: the emergence and influence of hubsMilos Radovanovic, Alexandros Nanopoulos, Mirjana Ivanovic. 109 [doi]
- Large-scale deep unsupervised learning using graphics processorsRajat Raina, Anand Madhavan, Andrew Y. Ng. 110 [doi]
- The Bayesian group-Lasso for analyzing contingency tablesSudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edgar Dahl, Volker Roth. 111 [doi]
- Supervised learning from multiple experts: whom to trust when everyone lies a bitVikas C. Raykar, Shipeng Yu, Linda H. Zhao, Anna K. Jerebko, Charles Florin, Gerardo Hermosillo Valadez, Luca Bogoni, Linda Moy. 112 [doi]
- Surrogate regret bounds for proper lossesMark D. Reid, Robert C. Williamson. 113 [doi]
- Learning structurally consistent undirected probabilistic graphical modelsSushmita Roy, Terran Lane, Margaret Werner-Washburne. 114 [doi]
- Ranking interesting subgroupsStefan Rueping. 115 [doi]
- Function factorization using warped Gaussian processesMikkel N. Schmidt. 116 [doi]
- Stochastic methods for ::::l::::::1:: regularized loss minimizationShai Shalev-Shwartz, Ambuj Tewari. 117 [doi]
- Structure preserving embeddingBlake Shaw, Tony Jebara. 118 [doi]
- Monte-Carlo simulation balancingDavid Silver, Gerald Tesauro. 119 [doi]
- Uncertainty sampling and transductive experimental design for active dual supervisionVikas Sindhwani, Prem Melville, Richard D. Lawrence. 120 [doi]
- Hilbert space embeddings of conditional distributions with applications to dynamical systemsLe Song, Jonathan Huang, Alexander J. Smola, Kenji Fukumizu. 121 [doi]
- Multi-assignment clustering for Boolean dataAndreas P. Streich, Mario Frank, David A. Basin, Joachim M. Buhmann. 122 [doi]
- A least squares formulation for a class of generalized eigenvalue problems in machine learningLiang Sun, Shuiwang Ji, Jieping Ye. 123 [doi]
- A simpler unified analysis of budget perceptronsIlya Sutskever. 124 [doi]
- Fast gradient-descent methods for temporal-difference learning with linear function approximationRichard S. Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, Eric Wiewiora. 125 [doi]
- Optimistic initialization and greediness lead to polynomial time learning in factored MDPsIstvan Szita, András Lörincz. 126 [doi]
- Discriminative ::::k::::-metricsArthur Szlam, Guillermo Sapiro. 127 [doi]
- Kernelized value function approximation for reinforcement learningGavin Taylor, Ronald Parr. 128 [doi]
- Factored conditional restricted Boltzmann Machines for modeling motion styleGraham W. Taylor, Geoffrey E. Hinton. 129 [doi]
- Using fast weights to improve persistent contrastive divergenceTijmen Tieleman, Geoffrey E. Hinton. 130 [doi]
- Structure learning with independent non-identically distributed dataRobert E. Tillman. 131 [doi]
- Robot trajectory optimization using approximate inferenceMarc Toussaint. 132 [doi]
- Ranking with ordered weighted pairwise classificationNicolas Usunier, David Buffoni, Patrick Gallinari. 133 [doi]
- More generality in efficient multiple kernel learningManik Varma, Bodla Rakesh Babu. 134 [doi]
- Information theoretic measures for clusterings comparison: is a correction for chance necessary?Xuan Vinh Nguyen, Julien Epps, James Bailey. 135 [doi]
- Model-free reinforcement learning as mixture learningNikos Vlassis, Marc Toussaint. 136 [doi]
- BoltzRank: learning to maximize expected ranking gainMaksims Volkovs, Richard S. Zemel. 137 [doi]
- K-means in space: a radiation sensitivity evaluationKiri L. Wagstaff, Benjamin Bornstein. 138 [doi]
- Evaluation methods for topic modelsHanna M. Wallach, Iain Murray, Ruslan Salakhutdinov, David M. Mimno. 139 [doi]
- Feature hashing for large scale multitask learningKilian Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, Josh Attenberg. 140 [doi]
- Herding dynamical weights to learnMax Welling. 141 [doi]
- A stochastic memoizer for sequence dataFrank Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh. 142 [doi]
- Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learningLinli Xu, Martha White, Dale Schuurmans. 143 [doi]
- Non-monotonic feature selectionZenglin Xu, Rong Jin, Jieping Ye, Michael R. Lyu, Irwin King. 144 [doi]
- Online learning by ellipsoid methodLiu Yang, Rong Jin, Jieping Ye. 145 [doi]
- Stochastic search using the natural gradientSun Yi, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber. 146 [doi]
- Learning structural SVMs with latent variablesChun-Nam John Yu, Thorsten Joachims. 147 [doi]
- Piecewise-stationary bandit problems with side observationsJia Yuan Yu, Shie Mannor. 148 [doi]
- Large-scale collaborative prediction using a nonparametric random effects modelKai Yu, John D. Lafferty, Shenghuo Zhu, Yihong Gong. 149 [doi]
- Robust feature extraction via information theoretic learningXiaotong Yuan, Bao-Gang Hu. 150 [doi]
- Interactively optimizing information retrieval systems as a dueling bandits problemYisong Yue, Thorsten Joachims. 151 [doi]
- Compositional noisy-logical learningAlan L. Yuille, Songfeng Zheng. 152 [doi]
- Discovering options from example trajectoriesPeng Zang, Peng Zhou, David Minnen, Charles Lee Isbell Jr.. 153 [doi]
- Learning instance specific distances using metric propagationDe-Chuan Zhan, Ming Li, Yu-Feng Li, Zhi-Hua Zhou. 154 [doi]
- Prototype vector machine for large scale semi-supervised learningKai Zhang, James T. Kwok, Bahram Parvin. 155 [doi]
- Learning non-redundant codebooks for classifying complex objectsWei Zhang, Akshat Surve, Xiaoli Fern, Thomas G. Dietterich. 156 [doi]
- Multi-instance learning by treating instances as non-I.I.D. samplesZhi-Hua Zhou, Yu-Yin Sun, Yu-Feng Li. 157 [doi]
- MedLDA: maximum margin supervised topic models for regression and classificationJun Zhu, Amr Ahmed, Eric P. Xing. 158 [doi]
- On primal and dual sparsity of Markov networksJun Zhu, Eric P. Xing. 159 [doi]
- SimpleNPKL: simple non-parametric kernel learningJinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi. 160 [doi]
- Invited talk: Can learning kernels help performance?Corinna Cortes. 161 [doi]
- Invited talk: Drifting games, boosting and online learningYoav Freund. 162 [doi]
- Workshop summary: Seventh annual workshop on Bayes applicationsJohn Mark Agosta, Russell Almond, Dennis M. Buede, Marek J. Druzdzel, Judy Goldsmith, Silja Renooij. 163 [doi]
- Workshop summary: Automated interpretation and modelling of cell imagesRobert F. Murphy, Chun-Nan Hsu, Loris Nanni. 164 [doi]
- Workshop summary: Workshop on learning feature hierarchiesKay Yu, Ruslan Salakhutdinov, Yann LeCun, Geoffrey E. Hinton, Yoshua Bengio. 165 [doi]
- Workshop summary: Results of the 2009 reinforcement learning competitionDavid Wingate, Carlos Diuk, Lihong Li, Matthew Taylor, Jordan Frank. 166 [doi]
- Workshop summary: The fourth workshop on evaluation methods for machine learningChris Drummond, Nathalie Japkowicz, William Klement, Sofus A. Macskassy. 167 [doi]
- Workshop summary: On-line learning with limited feedbackJean-Yves Audibert, Peter Auer, Alessandro Lazaric, Rémi Munos, Daniil Ryabko, Csaba Szepesvári. 168 [doi]
- Workshop summary: Numerical mathematics in machine learningMatthias Seeger, Suvrit Sra, John P. Cunningham. 169 [doi]
- Workshop summary: Abstraction in reinforcement learningÖzgür Simsek. 170 [doi]
- Workshop summary: Sparse methods for music audioDouglas Eck, Dan Ellis, Philippe Hamel. 171 [doi]
- Tutorial summary: Reductions in machine learningAlina Beygelzimer, John Langford, Bianca Zadrozny. 172 [doi]
- Tutorial summary: Convergence of natural dynamics to equilibriaEyal Even-Dar, Vahab S. Mirrokni. 173 [doi]
- Tutorial summary: Learning with dependencies between several response variablesVolker Tresp, Kai Yu. 174 [doi]
- Tutorial summary: Survey of boosting from an optimization perspectiveManfred K. Warmuth, S. V. N. Vishwanathan. 175 [doi]
- Tutorial summary: The neuroscience of reinforcement learningYael Niv. 176 [doi]
- Tutorial summary: Machine learning in IR: recent successes and new opportunitiesPaul Bennett, Misha Bilenko, Kevyn Collins-Thompson. 177 [doi]
- Tutorial summary: Active learningSanjoy Dasgupta, John Langford. 178 [doi]
- Tutorial summary: Large social and information networks: opportunities for MLJure Leskovec. 179 [doi]
- Tutorial summary: Structured prediction for natural language processingNoah A. Smith. 180 [doi]