Table of Contents

• Maximum Margin Multi-Instance LearningHua Wang, Heng Huang, Farhad Kamangar, Feiping Nie, Chris H. Q. Ding. 1-9 [doi]
• Shaping Level Sets with Submodular FunctionsFrancis Bach. 10-18 [doi]
• Nonlinear Inverse Reinforcement Learning with Gaussian ProcessesSergey Levine, Zoran Popovic, Vladlen Koltun. 19-27 [doi]
• Video Annotation and Tracking with Active LearningCarl Vondrick, Deva Ramanan. 28-36 [doi]
• On U-processes and clustering performanceStéphan Clémençon. 37-45 [doi]
• Penalty Decomposition Methods for Rank MinimizationYong Zhang, Zhaosong Lu. 46-54 [doi]
• Sparse Manifold Clustering and EmbeddingEhsan Elhamifar, René Vidal. 55-63 [doi]
• Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL ContractionSiwei Lyu. 64-72 [doi]
• Image Parsing with Stochastic Scene GrammarYibiao Zhao, Song Chun Zhu. 73-81 [doi]
• A Reinforcement Learning Theory for Homeostatic RegulationMehdi Keramati, Boris S. Gutkin. 82-90 [doi]
• Learning large-margin halfspaces with more malicious noisePhilip M. Long, Rocco A. Servedio. 91-99 [doi]
• On Strategy Stitching in Large Extensive Form Multiplayer GamesRichard G. Gibson, Duane Szafron. 100-108 [doi]
• Efficient Inference in Fully Connected CRFs with Gaussian Edge PotentialsPhilipp Krähenbühl, Vladlen Koltun. 109-117 [doi]
• Transfer Learning by Borrowing Examples for Multiclass Object DetectionJoseph J. Lim, Ruslan Salakhutdinov, Antonio Torralba. 118-126 [doi]
• Environmental statistics and the trade-off between model-based and TD learning in humansDylan A. Simon, Nathaniel D. Daw. 127-135 [doi]
• Variational Learning for Recurrent Spiking NetworksDanilo J. Rezende, Daan Wierstra, Wulfram Gerstner. 136-144 [doi]
• Multiple Instance Learning on Structured DataDan Zhang, Yan Liu, Luo Si, Jian Zhang, Richard D. Lawrence. 145-153 [doi]
• Manifold Precis: An Annealing Technique for Diverse Sampling of ManifoldsNitesh Shroff, Pavan K. Turaga, Rama Chellappa. 154-162 [doi]
• A Global Structural EM Algorithm for a Model of Cancer ProgressionAli Tofigh, Erik Sjlund, Mattias Höglund, Jens Lagergren. 163-171 [doi]
• Action-Gap Phenomenon in Reinforcement LearningAmir Massoud Farahmand. 172-180 [doi]
• Generalized Lasso based Approximation of Sparse Coding for Visual RecognitionNobuyuki Morioka, Shin'ichi Satoh. 181-189 [doi]
• Matrix Completion for Multi-label Image ClassificationRicardo Silveira Cabral, Fernando De la Torre, João Paulo Costeira, Alexandre Bernardino. 190-198 [doi]
• Multi-View Learning of Word Embeddings via CCAParamveer S. Dhillon, Dean P. Foster, Lyle H. Ungar. 199-207 [doi]
• Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise IndependentShinichi Nakajima, Masashi Sugiyama, S. Derin Babacan. 208-216 [doi]
• Estimating time-varying input signals and ion channel states from a single voltage trace of a neuronRyota Kobayashi, Yasuhiro Tsubo, Petr Lánský, Shigeru Shinomoto. 217-225 [doi]
• Additive Gaussian ProcessesDavid K. Duvenaud, Hannes Nickisch, Carl Edward Rasmussen. 226-234 [doi]
• Inferring Interaction Networks using the IBP applied to microRNA Target PredictionHai-son Le, Ziv Bar-Joseph. 235-243 [doi]
• Semantic Labeling of 3D Point Clouds for Indoor ScenesHema Swetha Koppula, Abhishek Anand, Thorsten Joachims, Ashutosh Saxena. 244-252 [doi]
• Learning Higher-Order Graph Structure with Features by Structure PenaltyShilin Ding, Grace Wahba, Xiaojin (Jerry) Zhu. 253-261 [doi]
• Analysis and Improvement of Policy Gradient EstimationTingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama. 262-270 [doi]
• Dimensionality Reduction Using the Sparse Linear ModelIoannis Gkioulekas, Todd Zickler. 271-279 [doi]
• Robust Multi-Class Gaussian Process ClassificationDaniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont. 280-288 [doi]
• Maximum Margin Multi-Label Structured PredictionChristoph H. Lampert. 289-297 [doi]
• Extracting Speaker-Specific Information with a Regularized Siamese Deep NetworkKe Chen, Ahmad Salman. 298-306 [doi]
• Thinning Measurement Models and Questionnaire DesignRicardo Silva. 307-315 [doi]
• Inductive reasoning about chimeric creaturesCharles Kemp. 316-324 [doi]
• Optimal Reinforcement Learning for Gaussian SystemsPhilipp Hennig. 325-333 [doi]
• A Denoising View of Matrix CompletionWeiran Wang, Miguel Á. Carreira-Perpiñán, Zhengdong Lu. 334-342 [doi]
• Efficient Online Learning via Randomized RoundingNicolò Cesa-Bianchi, Ohad Shamir. 343-351 [doi]
• Efficient Methods for Overlapping Group LassoLei Yuan, Jun Liu, Jieping Ye. 352-360 [doi]
• Differentially Private M-EstimatorsJing Lei. 361-369 [doi]
• Multiple Instance FilteringKamil Wnuk, Stefano Soatto. 370-378 [doi]
• Phase transition in the family of p-resistancesMorteza Alamgir, Ulrike von Luxburg. 379-387 [doi]
• Convergent Bounds on the Euclidean DistanceYoonho Hwang, Hee-Kap Ahn. 388-396 [doi]
• Heavy-tailed Distances for Gradient Based Image DescriptorsYangqing Jia, Trevor Darrell. 397-405 [doi]
• RTRMC: A Riemannian trust-region method for low-rank matrix completionNicolas Boumal, Pierre-Antoine Absil. 406-414 [doi]
• Expressive Power and Approximation Errors of Restricted Boltzmann MachinesGuido Montufar, Johannes Rauh, Nihat Ay. 415-423 [doi]
• History distribution matching method for predicting effectiveness of HIV combination therapiesJasmina Bogojeska. 424-432 [doi]
• Semi-supervised Regression via Parallel Field RegularizationBinbin Lin, Chiyuan Zhang, Xiaofei He. 433-441 [doi]
• Object Detection with Grammar ModelsRoss B. Girshick, Pedro F. Felzenszwalb, David A. McAllester. 442-450 [doi]
• Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine LearningFrancis Bach, Eric Moulines. 451-459 [doi]
• On fast approximate submodular minimizationStefanie Jegelka, Hui Lin, Jeff A. Bilmes. 460-468 [doi]
• Emergence of Multiplication in a Biophysical Model of a Wide-Field Visual Neuron for Computing Object Approaches: Dynamics, Peaks, & FitsMatthias S. Keil. 469-477 [doi]
• Efficient anomaly detection using bipartite k-NN graphsKumar Sricharan, Alfred O. Hero III. 478-486 [doi]
• Projection onto A Nonnegative Max-HeapJun Liu, Liang Sun, Jieping Ye. 487-495 [doi]
• Improving Topic Coherence with Regularized Topic ModelsDavid Newman, Edwin V. Bonilla, Wray L. Buntine. 496-504 [doi]
• A Two-Stage Weighting Framework for Multi-Source Domain AdaptationQian Sun, Rita Chattopadhyay, Sethuraman Panchanathan, Jieping Ye. 505-513 [doi]
• An ideal observer model for identifying the reference frame of objectsJoseph L. Austerweil, Abram L. Friesen, Thomas L. Griffiths. 514-522 [doi]
• Generalized Beta Mixtures of GaussiansArtin Armagan, David B. Dunson, Merlise Clyde. 523-531 [doi]
• Large-Scale Sparse Principal Component Analysis with Application to Text DataYouwei Zhang, Laurent El Ghaoui. 532-539 [doi]
• Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMCTrung-Thanh Pham, Tat-Jun Chin, Jin Yu, David Suter. 540-548 [doi]
• $\theta$-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene UnderstandingCongcong Li, Ashutosh Saxena, Tsuhan Chen. 549-557 [doi]
• CrowdclusteringRyan Gomes, Peter Welinder, Andreas Krause, Pietro Perona. 558-566 [doi]
• Fast and Balanced: Efficient Label Tree Learning for Large Scale Object RecognitionJia Deng, Sanjeev Satheesh, Alexander C. Berg, Fei-Fei Li. 567-575 [doi]
• Target Neighbor Consistent Feature Weighting for Nearest Neighbor ClassificationIchiro Takeuchi, Masashi Sugiyama. 576-584 [doi]
• The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel ClassifiersLuca Oneto, Davide Anguita, Alessandro Ghio, Sandro Ridella. 585-593 [doi]
• Relative Density-Ratio Estimation for Robust Distribution ComparisonMakoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama. 594-602 [doi]
• Solving Decision Problems with Limited InformationDenis Deratani Mauá, Cassio Polpo de Campos. 603-611 [doi]
• Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank RepresentationZhouchen Lin, Risheng Liu, Zhixun Su. 612-620 [doi]
• Learning a Tree of Metrics with Disjoint Visual FeaturesSung Ju Hwang, Kristen Grauman, Fei Sha. 621-629 [doi]
• Efficient inference in matrix-variate Gaussian models with \iid observation noiseOliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten M. Borgwardt. 630-638 [doi]
• On Causal Discovery with Cyclic Additive Noise ModelsJoris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf. 639-647 [doi]
• Learning to Agglomerate Superpixel HierarchiesViren Jain, Srinivas C. Turaga, Kevin L. Briggman, Moritz Helmstaedter, Winfried Denk, H. Sebastian Seung. 648-656 [doi]
• A Convergence Analysis of Log-Linear TrainingSimon Wiesler, Hermann Ney. 657-665 [doi]
• Shallow vs. Deep Sum-Product NetworksOlivier Delalleau, Yoshua Bengio. 666-674 [doi]
• Signal Estimation Under Random Time-Warpings and Nonlinear Signal AlignmentSebastian Kurtek, Anuj Srivastava, Wei Wu. 675-683 [doi]
• From Bandits to Experts: On the Value of Side-ObservationsShie Mannor, Ohad Shamir. 684-692 [doi]
• Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient DescentBenjamin Recht, Christopher Re, Stephen J. Wright, Feng Niu. 693-701 [doi]
• Clustered Multi-Task Learning Via Alternating Structure OptimizationJiayu Zhou, Jianhui Chen, Jieping Ye. 702-710 [doi]
• Why The Brain Separates Face Recognition From Object RecognitionJoel Z. Leibo, Jim Mutch, Tomaso Poggio. 711-719 [doi]
• Reinforcement Learning using Kernel-Based Stochastic FactorizationAndré da Motta Salles Barreto, Doina Precup, Joelle Pineau. 720-728 [doi]
• k-NN Regression Adapts to Local Intrinsic DimensionSamory Kpotufe. 729-737 [doi]
• Learning unbelievable probabilitiesXaq Pitkow, Yashar Ahmadian, Kenneth D. Miller. 738-746 [doi]
• A Machine Learning Approach to Predict Chemical ReactionsMatthew A. Kayala, Pierre Baldi. 747-755 [doi]
• Dynamical segmentation of single trials from population neural dataBiljana Petreska, Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani. 756-764 [doi]
• Recovering Intrinsic Images with a Global Sparsity Prior on ReflectancePeter V. Gehler, Carsten Rother, Martin Kiefel, Lumin Zhang, Bernhard Schölkopf. 765-773 [doi]
• Probabilistic Modeling of Dependencies Among Visual Short-Term Memory RepresentationsEmin Orhan, Robert A. Jacobs. 774-782 [doi]
• Optimistic Optimization of a Deterministic Function without the Knowledge of its SmoothnessRémi Munos. 783-791 [doi]
• Reconstructing Patterns of Information Diffusion from Incomplete ObservationsFlavio Chierichetti, Jon M. Kleinberg, David Liben-Nowell. 792-800 [doi]
• Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase DetectionRichard Socher, Eric H. Huang, Jeffrey Pennington, Andrew Y. Ng, Christopher D. Manning. 801-809 [doi]
• Active Learning Ranking from Pairwise Preferences with Almost Optimal Query ComplexityNir Ailon. 810-818 [doi]
• Modelling Genetic Variations using Fragmentation-Coagulation ProcessesYee Whye Teh, Charles Blundell, Lloyd T. Elliott. 819-827 [doi]
• Prediction strategies without lossMichael Kapralov, Rina Panigrahy. 828-836 [doi]
• Data Skeletonization via Reeb GraphsXiaoyin Ge, Issam Safa, Mikhail Belkin, Yusu Wang. 837-845 [doi]
• Information Rates and Optimal Decoding in Large Neural PopulationsKamiar Rahnama Rad, Liam Paninski. 846-854 [doi]
• Selective Prediction of Financial Trends with Hidden Markov ModelsDmitry Pidan, Ran El-Yaniv. 855-863 [doi]
• Maximal Cliques that Satisfy Hard Constraints with Application to Deformable Object Model LearningXinggang Wang, Xiang Bai, Xingwei Yang, Wenyu Liu, Longin Jan Latecki. 864-872 [doi]
• Distributed Delayed Stochastic OptimizationAlekh Agarwal, John C. Duchi. 873-881 [doi]
• Greedy Algorithms for Structurally Constrained High Dimensional ProblemsAmbuj Tewari, Pradeep D. Ravikumar, Inderjit S. Dhillon. 882-890 [doi]
• Newtron: an Efficient Bandit algorithm for Online Multiclass PredictionElad Hazan, Satyen Kale. 891-899 [doi]
• Learning Sparse Representations of High Dimensional Data on Large Scale DictionariesZhen James Xiang, Hao Xu, Peter J. Ramadge. 900-908 [doi]
• Minimax Localization of Structural Information in Large Noisy MatricesMladen Kolar, Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh. 909-917 [doi]
• Maximum Covariance Unfolding : Manifold Learning for Bimodal DataVijay Mahadevan, Chi-Wah Wong, Jose Costa Pereira, Tom Liu, Nuno Vasconcelos, Lawrence K. Saul. 918-926 [doi]
• Efficient Learning of Generalized Linear and Single Index Models with Isotonic RegressionSham M. Kakade, Adam Kalai, Varun Kanade, Ohad Shamir. 927-935 [doi]
• On the Analysis of Multi-Channel Neural Spike DataBo Chen, David E. Carlson, Lawrence Carin. 936-944 [doi]
• Learning Eigenvectors for FreeWouter M. Koolen, Wojciech Kotlowski, Manfred K. Warmuth. 945-953 [doi]
• Noise Thresholds for Spectral ClusteringSivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh. 954-962 [doi]
• The Kernel Beta ProcessLu Ren, Yingjian Wang, David B. Dunson, Lawrence Carin. 963-971 [doi]
• Statistical Performance of Convex Tensor DecompositionRyota Tomioka, Taiji Suzuki, Kohei Hayashi, Hisashi Kashima. 972-980 [doi]
• Probabilistic amplitude and frequency demodulationRichard Turner, Maneesh Sahani. 981-989 [doi]
• Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type OperatorsDominique C. Perrault-Joncas, Marina Meila. 990-998 [doi]
• Efficient coding of natural images with a population of noisy Linear-Nonlinear neuronsYan Karklin, Eero P. Simoncelli. 999-1007 [doi]
• Complexity of Inference in Latent Dirichlet AllocationDavid Sontag, Dan Roy. 1008-1016 [doi]
• ICA with Reconstruction Cost for Efficient Overcomplete Feature LearningQuoc V. Le, Alexandre Karpenko, Jiquan Ngiam, Andrew Y. Ng. 1017-1025 [doi]
• Lower Bounds for Passive and Active LearningMaxim Raginsky, Alexander Rakhlin. 1026-1034 [doi]
• Stochastic convex optimization with bandit feedbackAlekh Agarwal, Dean P. Foster, Daniel Hsu, Sham M. Kakade, Alexander Rakhlin. 1035-1043 [doi]
• Structure Learning for OptimizationShulin Yang, Ali Rahimi. 1044-1052 [doi]
• Inverting Grice's Maxims to Learn Rules from Natural Language ExtractionsShahed Sorower, Thomas G. Dietterich, Janardhan Rao Doppa, Walker Orr, Prasad Tadepalli, Xiaoli Fern. 1053-1061 [doi]
• Active Classification based on Value of ClassifierTianshi Gao, Daphne Koller. 1062-1070 [doi]
• Group Anomaly Detection using Flexible Genre ModelsLiang Xiong, Barnabás Póczos, Jeff G. Schneider. 1071-1079 [doi]
• Approximating Semidefinite Programs in Sublinear TimeDan Garber, Elad Hazan. 1080-1088 [doi]
• SpaRCS: Recovering low-rank and sparse matrices from compressive measurementsAndrew E. Waters, Aswin C. Sankaranarayanan, Richard G. Baraniuk. 1089-1097 [doi]
• Budgeted Optimization with Concurrent Stochastic-Duration ExperimentsJavad Azimi, Alan Fern, Xiaoli Fern. 1098-1106 [doi]
• Online Submodular Set Cover, Ranking, and Repeated Active LearningAndrew Guillory, Jeff A. Bilmes. 1107-1115 [doi]
• Structured sparse coding via lateral inhibitionArthur Szlam, Karol Gregor, Yann LeCun. 1116-1124 [doi]
• Sparse FilteringJiquan Ngiam, Pang Wei Koh, Zhenghao Chen, Sonia A. Bhaskar, Andrew Y. Ng. 1125-1133 [doi]
• Divide-and-Conquer Matrix FactorizationLester W. Mackey, Ameet Talwalkar, Michael I. Jordan. 1134-1142 [doi]
• Im2Text: Describing Images Using 1 Million Captioned PhotographsVicente Ordonez, Girish Kulkarni, Tamara L. Berg. 1143-1151 [doi]
• Nonstandard Interpretations of Probabilistic Programs for Efficient InferenceDavid Wingate, Noah D. Goodman, Andreas Stuhlmüller, Jeffrey Mark Siskind. 1152-1160 [doi]
• Collective Graphical ModelsDaniel R. Sheldon, Thomas G. Dietterich. 1161-1169 [doi]
• Metric Learning with Multiple KernelsJun Wang 0017, Huyen Do, Adam Woznica, Alexandros Kalousis. 1170-1178 [doi]
• ShareBoost: Efficient multiclass learning with feature sharingShai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua. 1179-1187 [doi]
• Active dendrites: adaptation to spike-based communicationBalázs Ujfalussy, Máté Lengyel. 1188-1196 [doi]
• Message-Passing for Approximate MAP Inference with Latent VariablesJiarong Jiang, Piyush Rai, Hal Daumé III. 1197-1205 [doi]
• A More Powerful Two-Sample Test in High Dimensions using Random ProjectionMiles Lopes, Laurent Jacob, Martin J. Wainwright. 1206-1214 [doi]
• Orthogonal Matching Pursuit with ReplacementPrateek Jain 0002, Ambuj Tewari, Inderjit S. Dhillon. 1215-1223 [doi]
• Composite Multiclass LossesElodie Vernet, Robert C. Williamson, Mark D. Reid. 1224-1232 [doi]
• Beating SGD: Learning SVMs in Sublinear TimeElad Hazan, Tomer Koren, Nati Srebro. 1233-1241 [doi]
• Greedy Model AveragingDong Dai, Tong Zhang 0001. 1242-1250 [doi]
• Large-Scale Category Structure Aware Image CategorizationBin Zhao, Fei-Fei Li, Eric P. Xing. 1251-1259 [doi]
• On the accuracy of l1-filtering of signals with block-sparse structureAnatoli Iouditski, Fatma Kilinç-Karzan, Arkadi Nemirovski, Boris T. Polyak. 1260-1268 [doi]
• Multilinear Subspace Regression: An Orthogonal Tensor Decomposition ApproachQibin Zhao, Cesar F. Caiafa, Danilo P. Mandic, Liqing Zhang, Tonio Ball, Andreas Schulze-Bonhage, Andrzej Cichocki. 1269-1277 [doi]
• Finite Time Analysis of Stratified Sampling for Monte CarloAlexandra Carpentier, Rémi Munos. 1278-1286 [doi]
• Monte Carlo Value Iteration with Macro-ActionsZhan Wei Lim, David Hsu, Lee Sun. 1287-1295 [doi]
• Structured Learning for Cell TrackingXinghua Lou, Fred A. Hamprecht. 1296-1304 [doi]
• Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memoriesCristina Savin, Peter Dayan, Máté Lengyel. 1305-1313 [doi]
• Algorithms and hardness results for parallel large margin learningRocco A. Servedio, Philip M. Long. 1314-1322 [doi]
• Portmanteau Vocabularies for Multi-Cue Image RepresentationFahad Shahbaz Khan, Joost van de Weijer, Andrew D. Bagdanov, Maria Vanrell. 1323-1331 [doi]
• Boosting with Maximum Adaptive SamplingCharles Dubout, François Fleuret. 1332-1340 [doi]
• Gaussian Process Training with Input NoiseAndrew McHutchon, Carl Edward Rasmussen. 1341-1349 [doi]
• Empirical models of spiking in neural populationsJakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani. 1350-1358 [doi]
• Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex ActivitiesAngela Yao, Juergen Gall, Luc J. Van Gool, Raquel Urtasun. 1359-1367 [doi]
• Bayesian Partitioning of Large-Scale Distance DataDavid Adametz, Volker Roth. 1368-1376 [doi]
• From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear ModelsSkander Mensi, Richard Naud, Wulfram Gerstner. 1377-1385 [doi]
• On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic InferenceGuy Van den Broeck. 1386-1394 [doi]
• Hierarchical Topic Modeling for Analysis of Time-Evolving Personal ChoicesXianXing Zhang, David B. Dunson, Lawrence Carin. 1395-1403 [doi]
• An Exact Algorithm for F-Measure MaximizationKrzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier. 1404-1412 [doi]
• Co-regularized Multi-view Spectral ClusteringAbhishek Kumar, Piyush Rai, Hal Daumé III. 1413-1421 [doi]
• Sequence learning with hidden units in spiking neural networksJohanni Brea, Walter Senn, Jean-Pascal Pfister. 1422-1430 [doi]
• Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination AnalysisShuai Huang, Jing Li, Jieping Ye, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman. 1431-1439 [doi]
• A blind sparse deconvolution method for neural spike identificationChaitanya Ekanadham, Daniel Tranchina, Eero P. Simoncelli. 1440-1448 [doi]
• How Do Humans Teach: On Curriculum Learning and Teaching DimensionFaisal Khan, Xiaojin (Jerry) Zhu, Bilge Mutlu. 1449-1457 [doi]
• Convergence Rates of Inexact Proximal-Gradient Methods for Convex OptimizationMark W. Schmidt, Nicolas Le Roux, Francis Bach. 1458-1466 [doi]
• Joint 3D Estimation of Objects and Scene LayoutAndreas Geiger, Christian Wojek, Raquel Urtasun. 1467-1475 [doi]
• Spatial distance dependent Chinese restaurant processes for image segmentationSoumya Ghosh, Andrei B. Ungureanu, Erik B. Sudderth, David M. Blei. 1476-1484 [doi]
• Pylon Model for Semantic SegmentationVictor S. Lempitsky, Andrea Vedaldi, Andrew Zisserman. 1485-1493 [doi]
• t-divergence Based Approximate InferenceNan Ding, S. V. N. Vishwanathan, Yuan (Alan) Qi. 1494-1502 [doi]
• Learning person-object interactions for action recognition in still imagesVincent Delaitre, Josef Sivic, Ivan Laptev. 1503-1511 [doi]
• Submodular Multi-Label LearningJames Petterson, Tibério S. Caetano. 1512-1520 [doi]
• Uniqueness of Belief Propagation on Signed GraphsYusuke Watanabe. 1521-1529 [doi]
• Higher-Order Correlation Clustering for Image SegmentationSungwoong Kim, Sebastian Nowozin, Pushmeet Kohli, Chang Dong Yoo. 1530-1538 [doi]
• Optimal learning rates for least squares SVMs using Gaussian kernelsMona Eberts, Ingo Steinwart. 1539-1547 [doi]
• Learning Auto-regressive Models from Sequence and Non-sequence DataTzu-Kuo Huang, Jeff G. Schneider. 1548-1556 [doi]
• Committing BanditsLoc Bui, Ramesh Johari, Shie Mannor. 1557-1565 [doi]
• Energetically Optimal Action PotentialsMartin B. Stemmler, Biswa Sengupta, Simon B. Laughlin, Jeremy E. Niven. 1566-1574 [doi]
• Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel LearningTaiji Suzuki. 1575-1583 [doi]
• See the Tree Through the Lines: The Shazoo AlgorithmFabio Vitale, Nicolò Cesa-Bianchi, Claudio Gentile, Giovanni Zappella. 1584-1592 [doi]
• The Fast Convergence of BoostingMatus Telgarsky. 1593-1601 [doi]
• Multi-armed bandits on implicit metric spacesAleksandrs Slivkins. 1602-1610 [doi]
• Learning Anchor Planes for ClassificationZiming Zhang, Lubor Ladicky, Philip H. S. Torr, Amir Saffari. 1611-1619 [doi]
• Infinite Latent SVM for Classification and Multi-task LearningJun Zhu, Ning Chen, Eric P. Xing. 1620-1628 [doi]
• Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann MachinesMatthew D. Zeiler, Graham W. Taylor, Leonid Sigal, Iain Matthews, Rob Fergus. 1629-1637 [doi]
• Universal low-rank matrix recovery from Pauli measurementsYi-Kai Liu. 1638-1646 [doi]
• Better Mini-Batch Algorithms via Accelerated Gradient MethodsAndrew Cotter, Ohad Shamir, Nati Srebro, Karthik Sridharan. 1647-1655 [doi]
• Adaptive HedgeTim van Erven, Peter Grunwald, Wouter M. Koolen, Steven de Rooij. 1656-1664 [doi]
• Agnostic Selective ClassificationYair Wiener, Ran El-Yaniv. 1665-1673 [doi]
• Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMsArmen E. Allahverdyan, Aram Galstyan. 1674-1682 [doi]
• PAC-Bayesian Analysis of Contextual BanditsYevgeny Seldin, Peter Auer, François Laviolette, John Shawe-Taylor, Ronald Ortner. 1683-1691 [doi]
• Bayesian Spike-Triggered Covariance AnalysisIl Memming Park, Jonathan W. Pillow. 1692-1700 [doi]
• Non-conjugate Variational Message Passing for Multinomial and Binary RegressionDavid A. Knowles, Tom Minka. 1701-1709 [doi]
• Learning to Search Efficiently in High DimensionsZhen Li, Huazhong Ning, Liangliang Cao, Tong Zhang 0001, Yihong Gong, Thomas S. Huang. 1710-1718 [doi]
• A Non-Parametric Approach to Dynamic ProgrammingOliver Kroemer, Jan Peters. 1719-1727 [doi]
• Advice Refinement in Knowledge-Based SVMsGautam Kunapuli, Richard Maclin, Jude W. Shavlik. 1728-1736 [doi]
• Kernel Bayes' RuleKenji Fukumizu, Le Song, Arthur Gretton. 1737-1745 [doi]
• Transfer from Multiple MDPsAlessandro Lazaric, Marcello Restelli. 1746-1754 [doi]
• Sparse Bayesian Multi-Task LearningCédric Archambeau, Shengbo Guo, Onno Zoeter. 1755-1763 [doi]
• Online Learning: Stochastic, Constrained, and Smoothed AdversariesAlexander Rakhlin, Karthik Sridharan, Ambuj Tewari. 1764-1772 [doi]
• Learning in Hilbert vs. Banach Spaces: A Measure Embedding ViewpointBharath K. Sriperumbudur, Kenji Fukumizu, Gert R. G. Lanckriet. 1773-1781 [doi]
• Sparse Recovery with Brownian SensingAlexandra Carpentier, Odalric-Ambrym Maillard, Rémi Munos. 1782-1790 [doi]
• An Unsupervised Decontamination Procedure For Improving The Reliability Of Human JudgmentsMichael C. Mozer, Benjamin Link, Harold Pashler. 1791-1799 [doi]
• Bayesian Bias Mitigation for CrowdsourcingFabian L. Wauthier, Michael I. Jordan. 1800-1808 [doi]
• Ranking annotators for crowdsourced labeling tasksVikas C. Raykar, Shipeng Yu. 1809-1817 [doi]
• Clustering via Dirichlet Process Mixture Models for Portable Skill DiscoveryScott Niekum, Andrew G. Barto. 1818-1826 [doi]
• Probabilistic Joint Image Segmentation and LabelingAdrian Ion, João Carreira, Cristian Sminchisescu. 1827-1835 [doi]
• Variance Reduction in Monte-Carlo Tree SearchJoel Veness, Marc Lanctot, Michael H. Bowling. 1836-1844 [doi]
• Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent RegressorsChun-Nam Yu, Russell Greiner, Hsiu-Chin Lin, Vickie Baracos. 1845-1853 [doi]
• An Application of Tree-Structured Expectation Propagation for Channel DecodingPablo M. Olmos, Luis Salamanca, Juan José Murillo-Fuentes, Fernando Pérez-Cruz. 1854-1862 [doi]
• High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary ConditionsAnimashree Anandkumar, Vincent Y. F. Tan, Alan S. Willsky. 1863-1871 [doi]
• Structural equations and divisive normalization for energy-dependent component analysisJunichiro Hirayama, Aapo Hyvärinen. 1872-1880 [doi]
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• Variance Penalizing AdaBoostPannagadatta K. Shivaswamy, Tony Jebara. 1908-1916 [doi]
• Efficient Offline Communication Policies for Factored Multiagent POMDPsJoão V. Messias, Matthijs T. J. Spaan, Pedro U. Lima. 1917-1925 [doi]
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• Policy Gradient Coagent NetworksPhilip S. Thomas. 1944-1952 [doi]
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• A Model for Temporal Dependencies in Event StreamsAsela Gunawardana, Christopher Meek, Puyang Xu. 1962-1970 [doi]
• Unsupervised learning models of primary cortical receptive fields and receptive field plasticityAndrew M. Saxe, Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Y. Ng. 1971-1979 [doi]
• The Doubly Correlated Nonparametric Topic ModelDae-Il Kim, Erik B. Sudderth. 1980-1988 [doi]
• MAP Inference for Bayesian Inverse Reinforcement LearningJaedeug Choi, Kee-Eung Kim. 1989-1997 [doi]
• Similarity-based Learning via Data Driven EmbeddingsPurushottam Kar, Prateek Jain 0002. 1998-2006 [doi]
• Predicting Dynamic DifficultyOlana Missura, Thomas Gärtner. 2007-2015 [doi]
• Sparse Estimation with Structured DictionariesDavid P. Wipf. 2016-2024 [doi]
• Spectral Methods for Learning Multivariate Latent Tree StructureAnimashree Anandkumar, Kamalika Chaudhuri, Daniel Hsu, Sham M. Kakade, Le Song, Tong Zhang 0001. 2025-2033 [doi]
• How biased are maximum entropy models?Jakob H. Macke, Iain Murray, Peter E. Latham. 2034-2042 [doi]
• Active learning of neural response functions with Gaussian processesMijung Park, Greg Horwitz, Jonathan W. Pillow. 2043-2051 [doi]
• Priors over Recurrent Continuous Time ProcessesArdavan Saeedi, Alexandre Bouchard-Côté. 2052-2060 [doi]
• Learning to Learn with Compound HD ModelsRuslan Salakhutdinov, Joshua B. Tenenbaum, Antonio Torralba. 2061-2069 [doi]
• Anatomically Constrained Decoding of Finger Flexion from Electrocorticographic SignalsZuoguan Wang, Gerwin Schalk, Qiang Ji. 2070-2078 [doi]
• Active Learning with a Drifting DistributionLiu Yang. 2079-2087 [doi]
• PiCoDes: Learning a Compact Code for Novel-Category RecognitionAlessandro Bergamo, Lorenzo Torresani, Andrew W. Fitzgibbon. 2088-2096 [doi]
• Confidence Sets for Network StructureDavid S. Choi, Patrick J. Wolfe, Edoardo M. Airoldi. 2097-2105 [doi]
• Prismatic Algorithm for Discrete D.C. Programming ProblemYoshinobu Kawahara, Takashi Washio. 2106-2114 [doi]
• Hierarchical Matching Pursuit for Image Classification: Architecture and Fast AlgorithmsLiefeng Bo, Xiaofeng Ren, Dieter Fox. 2115-2123 [doi]
• Multiclass Boosting: Theory and AlgorithmsMohammad J. Saberian, Nuno Vasconcelos. 2124-2132 [doi]
• Learning with the weighted trace-norm under arbitrary sampling distributionsRina Foygel, Ruslan Salakhutdinov, Ohad Shamir, Nati Srebro. 2133-2141 [doi]
• Scalable Training of Mixture Models via CoresetsDan Feldman, Matthew Faulkner, Andreas Krause. 2142-2150 [doi]
• Generalised Coupled Tensor FactorisationY. Kenan Yilmaz, Ali Taylan Cemgil, Umut Simsekli. 2151-2159 [doi]
• Nearest Neighbor based Greedy Coordinate DescentInderjit S. Dhillon, Pradeep D. Ravikumar, Ambuj Tewari. 2160-2168 [doi]
• The Fixed Points of Off-Policy TDJ. Zico Kolter. 2169-2177 [doi]
• Generalizing from Several Related Classification Tasks to a New Unlabeled SampleGilles Blanchard, Gyemin Lee, Clayton Scott. 2178-2186 [doi]
• Trace Lasso: a trace norm regularization for correlated designsEdouard Grave, Guillaume Obozinski, Francis Bach. 2187-2195 [doi]
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• Active Ranking using Pairwise ComparisonsKevin G. Jamieson, Robert D. Nowak. 2240-2248 [doi]
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• Selecting Receptive Fields in Deep NetworksAdam Coates, Andrew Y. Ng. 2528-2536 [doi]
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• Query-Aware MCMCMichael L. Wick, Andrew McCallum. 2564-2572 [doi]
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