Table of Contents

• An Optimal Policy for Target Localization with Application to Electron MicroscopyRaphael Sznitman, Aurélien Lucchi, Peter Frazier, Bruno Jedynak, Pascal Fua. 1-9 [doi]
• Guided Policy SearchSergey Levine, Vladlen Koltun. 1-9 [doi]
• Mixture of Mutually Exciting Processes for Viral DiffusionShuang-Hong Yang, Hongyuan Zha. 1-9 [doi]
• Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised LearningGang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama. 10-18 [doi]
• Gaussian Process Vine Copulas for Multivariate DependenceDavid Lopez-Paz, José Miguel Hernández-Lobato, Zoubin Ghahramani. 10-18 [doi]
• Domain Generalization via Invariant Feature RepresentationKrikamol Muandet, David Balduzzi, Bernhard Schölkopf. 10-18 [doi]
• Stochastic Simultaneous Optimistic OptimizationMichal Valko, Alexandra Carpentier, Rémi Munos. 19-27 [doi]
• A Spectral Learning Approach to Range-Only SLAMByron Boots, Geoffrey J. Gordon. 19-26 [doi]
• Gossip-based distributed stochastic bandit algorithmsBalázs Szörényi, Róbert Busa-Fekete, István Hegedüs, Róbert Ormándi, Márk Jelasity, Balázs Kégl. 19-27 [doi]
• Near-Optimal Bounds for Cross-Validation via Loss StabilityRavi Kumar, Daniel Lokshtanov, Sergei Vassilvitskii, Andrea Vattani. 27-35 [doi]
• Toward Optimal Stratification for Stratified Monte-Carlo IntegrationAlexandra Carpentier, Rémi Munos. 28-36 [doi]
• The Sample-Complexity of General Reinforcement LearningTor Lattimore, Marcus Hutter, Peter Sunehag. 28-36 [doi]
• Sparsity-Based Generalization Bounds for Predictive Sparse CodingNishant Ajay Mehta, Alexander G. Gray. 36-44 [doi]
• Hierarchical Regularization Cascade for Joint LearningAlon Zweig, Daphna Weinshall. 37-45 [doi]
• A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization ProblemsPinghua Gong, Changshui Zhang, Zhaosong Lu, Jianhua Huang, Jieping Ye. 37-45 [doi]
• Sparse Uncorrelated Linear Discriminant AnalysisXiaowei Zhang, Delin Chu. 45-52 [doi]
• Thurstonian Boltzmann Machines: Learning from Multiple InequalitiesTruyen Tran, Dinh Q. Phung, Svetha Venkatesh. 46-54 [doi]
• Multi-Class Classification with Maximum Margin Multiple KernelCorinna Cortes, Mehryar Mohri, Afshin Rostamizadeh. 46-54 [doi]
• Block-Coordinate Frank-Wolfe Optimization for Structural SVMsSimon Lacoste-Julien, Martin Jaggi, Mark W. Schmidt, Patrick Pletscher. 53-61 [doi]
• A Variational Approximation for Topic Modeling of Hierarchical CorporaDo-kyum Kim, Geoffrey M. Voelker, Lawrence K. Saul. 55-63 [doi]
• Bayesian Games for Adversarial Regression ProblemsMichael Großhans, Christoph Sawade, Michael Brückner, Tobias Scheffer. 55-63 [doi]
• Fast Probabilistic Optimization from Noisy GradientsPhilipp Hennig. 62-70 [doi]
• Forecastable Component AnalysisGeorg M. Goerg. 64-72 [doi]
• Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in CrowdsourcingXi Chen, Qihang Lin, Dengyong Zhou. 64-72 [doi]
• Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging SchemesOhad Shamir, Tong Zhang 0001. 71-79 [doi]
• Markov Network Estimation From Multi-attribute DataMladen Kolar, Han Liu, Eric P. Xing. 73-81 [doi]
• Ellipsoidal Multiple Instance LearningGabriel Krummenacher, Cheng Soon Ong, Joachim M. Buhmann. 73-81 [doi]
• Stochastic Alternating Direction Method of MultipliersHua Ouyang, Niao He, Long Tran, Alexander G. Gray. 80-88 [doi]
• MILEAGE: Multiple Instance LEArning with Global EmbeddingDan Zhang, Jingrui He, Luo Si, Richard D. Lawrence. 82-90 [doi]
• Local Low-Rank Matrix ApproximationJoonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer. 82-90 [doi]
• Noisy Sparse Subspace ClusteringYu-Xiang Wang, Huan Xu. 89-97 [doi]
• Guaranteed Sparse Recovery under Linear TransformationJi Liu 0002, Lei Yuan, Jieping Ye. 91-99 [doi]
• Generic Exploration and K-armed Voting BanditsTanguy Urvoy, Fabrice Clérot, Raphaël Féraud, Sami Naamane. 91-99 [doi]
• Parallel Markov Chain Monte Carlo for Nonparametric Mixture ModelsSinead Williamson, Avinava Dubey, Eric P. Xing. 98-106 [doi]
• Learning invariant features by harnessing the aperture problemRoland Memisevic, Georgios Exarchakis. 100-108 [doi]
• A unifying framework for vector-valued manifold regularization and multi-view learningHa Quang Minh, Loris Bazzani, Vittorio Murino. 100-108 [doi]
• Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output PredictionSébastien Giguère, François Laviolette, Mario Marchand, Khadidja Sylla. 107-114 [doi]
• Learning Connections in Financial Time SeriesGartheeban Ganeshapillai, John V. Guttag, Andrew Lo. 109-117 [doi]
• Efficient Ranking from Pairwise ComparisonsFabian L. Wauthier, Michael I. Jordan, Nebojsa Jojic. 109-117 [doi]
• Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision ArchitecturesJames Bergstra, Daniel Yamins, David D. Cox. 115-123 [doi]
• Fast dropout trainingSida Wang, Christopher D. Manning. 118-126 [doi]
• Differentially Private Learning with KernelsPrateek Jain 0002, Abhradeep Thakurta. 118-126 [doi]
• Gibbs Max-Margin Topic Models with Fast Sampling AlgorithmsJun Zhu, Ning Chen, Hugh Perkins, Bo Zhang. 124-132 [doi]
• Thompson Sampling for Contextual Bandits with Linear PayoffsShipra Agrawal, Navin Goyal. 127-135 [doi]
• Scalable Optimization of Neighbor Embedding for VisualizationZhirong Yang, Jaakko Peltonen, Samuel Kaski. 127-135 [doi]
• Cost-Sensitive Tree of ClassifiersZhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen. 133-141 [doi]
• Learning Multiple Behaviors from Unlabeled Demonstrations in a Latent Controller SpaceJavier Almingol, Luis Montesano, Manuel Lopes. 136-144 [doi]
• Precision-recall space to correct external indices for biclusteringBlaise Hanczar, Mohamed Nadif. 136-144 [doi]
• Learning Hash Functions Using Column GenerationXi Li, Guosheng Lin, Chunhua Shen, Anton van den Hengel, Anthony R. Dick. 142-150 [doi]
• Inference algorithms for pattern-based CRFs on sequence dataVladimir Kolmogorov, Rustem Takhanov. 145-153 [doi]
• Monochromatic Bi-ClusteringSharon Wulff, Ruth Urner, Shai Ben-David. 145-153 [doi]
• Combinatorial Multi-Armed Bandit: General Framework and ApplicationsWei Chen, Yajun Wang, Yang Yuan. 151-159 [doi]
• One-Bit Compressed Sensing: Provable Support and Vector RecoverySivakant Gopi, Praneeth Netrapalli, Prateek Jain 0002, Aditya V. Nori. 154-162 [doi]
• Gated Autoencoders with Tied Input WeightsDroniou Alain, Sigaud Olivier. 154-162 [doi]
• Near-optimal Batch Mode Active Learning and Adaptive Submodular OptimizationYuxin Chen, Andreas Krause. 160-168 [doi]
• Strict Monotonicity of Sum of Squares Error and Normalized Cut in the Lattice of ClusteringsNicola Rebagliati. 163-171 [doi]
• Tensor AnalyzersYichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton. 163-171 [doi]
• Convex formulations of radius-margin based Support Vector MachinesHuyen Do, Alexandros Kalousis. 169-177 [doi]
• Transition Matrix Estimation in High Dimensional Time SeriesFang Han, Han Liu. 172-180 [doi]
• Learning Sparse Penalties for Change-point Detection using Max Margin Interval RegressionToby Hocking, Guillem Rigaill, Jean-Philippe Vert, Francis Bach. 172-180 [doi]
• Modelling Sparse Dynamical Systems with Compressed Predictive State RepresentationsWilliam L. Hamilton, Mahdi Milani Fard, Joelle Pineau. 178-186 [doi]
• Learning from Human-Generated ListsKwang-Sung Jun, Xiaojin (Jerry) Zhu, Burr Settles, Timothy T. Rogers. 181-189 [doi]
• Label Partitioning For Sublinear RankingJason Weston, Ameesh Makadia, Hector Yee. 181-189 [doi]
• A Machine Learning Framework for Programming by ExampleAditya Krishna Menon, Omer Tamuz, Sumit Gulwani, Butler W. Lampson, Adam Kalai. 187-195 [doi]
• Subproblem-Tree Calibration: A Unified Approach to Max-Product Message PassingHuayan Wang, Daphne Koller. 190-198 [doi]
• A Fast and Exact Energy Minimization Algorithm for Cycle MRFsHuayan Wang, Daphne Koller. 190-198 [doi]
• Discriminatively Activated SparseletsRoss B. Girshick, Hyun Oh Song, Trevor Darrell. 196-204 [doi]
• Collaborative hyperparameter tuningRémi Bardenet, Mátyás Brendel, Balázs Kégl, Michèle Sebag. 199-207 [doi]
• Stochastic k-Neighborhood Selection for Supervised and Unsupervised LearningDaniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel. 199-207 [doi]
• The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear ClassificationOfir Pele, Ben Taskar, Amir Globerson, Michael Werman. 205-213 [doi]
• An Efficient Posterior Regularized Latent Variable Model for Interactive Sound Source SeparationNicholas J. Bryan, Gautham J. Mysore. 208-216 [doi]
• SADA: A General Framework to Support Robust Causation DiscoveryRuichu Cai, Zhenjie Zhang, Zhifeng Hao. 208-216 [doi]
• Fixed-Point Model For Structured LabelingQuannan Li, Jingdong Wang, David P. Wipf, Zhuowen Tu. 214-221 [doi]
• Estimating Unknown Sparsity in Compressed SensingMiles Lopes. 217-225 [doi]
• Learning and Selecting Features Jointly with Point-wise Gated Boltzmann MachinesKihyuk Sohn, Guanyu Zhou, Chansoo Lee, Honglak Lee. 217-225 [doi]
• Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain AdaptationBoqing Gong, Kristen Grauman, Fei Sha. 222-230 [doi]
• Sequential Bayesian SearchZheng Wen, Branislav Kveton, Brian Eriksson, Sandilya Bhamidipati. 226-234 [doi]
• MAD-Bayes: MAP-based Asymptotic Derivations from BayesTamara Broderick, Brian Kulis, Michael Jordan. 226-234 [doi]
• Fast Conical Hull Algorithms for Near-separable Non-negative Matrix FactorizationAbhishek Kumar, Vikas Sindhwani, Prabhanjan Kambadur. 231-239 [doi]
• The Most Generative Maximum Margin Bayesian NetworksRobert Peharz, Sebastian Tschiatschek, Franz Pernkopf. 235-243 [doi]
• Sparse projections onto the simplexAnastasios T. Kyrillidis, Stephen Becker, Volkan Cevher, Christoph Koch. 235-243 [doi]
• Principal Component Analysis on non-Gaussian Dependent DataFang Han, Han Liu. 240-248 [doi]
• Fastfood - Computing Hilbert Space Expansions in loglinear timeQuoc V. Le, Tamás Sarlós, Alexander J. Smola. 244-252 [doi]
• Modeling Musical Influence with Topic ModelsUri Shalit, Daphna Weinshall, Gal Chechik. 244-252 [doi]
• Learning Linear Bayesian Networks with Latent VariablesAnimashree Anandkumar, Daniel Hsu, Adel Javanmard, Sham Kakade. 249-257 [doi]
• Joint Transfer and Batch-mode Active LearningRita Chattopadhyay, Wei Fan, Ian Davidson, Sethuraman Panchanathan, Jieping Ye. 253-261 [doi]
• Subtle Topic Models and Discovering Subtly Manifested Software Concerns AutomaticallyMrinal Kanti Das, Suparna Bhattacharya, Chiranjib Bhattacharyya, Kanchi Gopinath. 253-261 [doi]
• Multiple Identifications in Multi-Armed BanditsSébastien Bubeck, Tengyao Wang, Nitin Viswanathan. 258-265 [doi]
• Message passing with l1 penalized KL minimizationYuan Qi, Yandong Guo. 262-270 [doi]
• Exploring the Mind: Integrating Questionnaires and fMRIEsther Salazar, Ryan Bogdan, Adam Gorka, Ahmad Hariri, Lawrence Carin. 262-270 [doi]
• Learning Optimally Sparse Support Vector MachinesAndrew Cotter, Shai Shalev-Shwartz, Nati Srebro. 266-274 [doi]
• A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversionsQuoc Tran-Dinh, Anastasios T. Kyrillidis, Volkan Cevher. 271-279 [doi]
• Mean Reversion with a Variance ThresholdMarco Cuturi, Alexandre d'Aspremont. 271-279 [doi]
• Dynamic Probabilistic Models for Latent Feature Propagation in Social NetworksCreighton Heaukulani, Zoubin Ghahramani. 275-283 [doi]
• Top-down particle filtering for Bayesian decision treesBalaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh. 280-288 [doi]
• A Practical Algorithm for Topic Modeling with Provable GuaranteesSanjeev Arora, Rong Ge, Yonatan Halpern, David M. Mimno, Ankur Moitra, David Sontag, Yichen Wu, Michael Zhu. 280-288 [doi]
• Efficient Sparse Group Feature Selection via Nonconvex OptimizationShuo Xiang, Xiaoshen Tong, Jieping Ye. 284-292 [doi]
• Smooth Sparse Coding via Marginal Regression for Learning Sparse RepresentationsKrishnakumar Balasubramanian, Kai Yu, Guy Lebanon. 289-297 [doi]
• Distributed training of Large-scale Logistic modelsSiddharth Gopal, Yiming Yang. 289-297 [doi]
• Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation ModelMin Xiao, Yuhong Guo. 293-301 [doi]
• An Adaptive Learning Rate for Stochastic Variational InferenceRajesh Ranganath, Chong Wang, David M. Blei, Eric P. Xing. 298-306 [doi]
• Robust and Discriminative Self-Taught LearningHua Wang, Feiping Nie, Heng Huang. 298-306 [doi]
• Maximum Variance Correction with Application to A* SearchWenlin Chen, Kilian Q. Weinberger, Yixin Chen. 302-310 [doi]
• Margins, Shrinkage, and BoostingMatus Telgarsky. 307-315 [doi]
• Safe Policy IterationMatteo Pirotta, Marcello Restelli, Alessio Pecorino, Daniele Calandriello. 307-315 [doi]
• Adaptive Sparsity in Gaussian Graphical ModelsEleanor Wong, Suyash P. Awate, P. Thomas Fletcher. 311-319 [doi]
• Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target AlignmentBilly Chang, Uwe Krüger, Rafal Kustra, Junping Zhang. 316-324 [doi]
• Unfolding Latent Tree Structures using 4th Order TensorsMariya Ishteva, Haesun Park, Le Song. 316-324 [doi]
• Average Reward Optimization Objective In Partially Observable DomainsYuri Grinberg, Doina Precup. 320-328 [doi]
• Learning Fair RepresentationsRichard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, Cynthia Dwork. 325-333 [doi]
• Large-Scale Learning with Less RAM via RandomizationDaniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young. 325-333 [doi]
• Feature Selection in High-Dimensional ClassificationMladen Kolar, Han Liu. 329-337 [doi]
• Taming the Curse of Dimensionality: Discrete Integration by Hashing and OptimizationStefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman. 334-342 [doi]
• Hierarchical Tensor Decomposition of Latent Tree Graphical ModelsLe Song, Mariya Ishteva, Ankur P. Parikh, Eric P. Xing, Haesun Park. 334-342 [doi]
• Human BoostingHarsh Pareek, Pradeep Ravikumar. 338-346 [doi]
• No more pesky learning ratesTom Schaul, Sixin Zhang, Yann LeCun. 343-351 [doi]
• Sparse coding for multitask and transfer learningAndreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes. 343-351 [doi]
• Efficient Dimensionality Reduction for Canonical Correlation AnalysisHaim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias. 347-355 [doi]
• Direct Modeling of Complex Invariances for Visual Object FeaturesKa Yu Hui. 352-360 [doi]
• Multi-View Clustering and Feature Learning via Structured SparsityHua Wang, Feiping Nie, Heng Huang. 352-360 [doi]
• Parsing epileptic events using a Markov switching process model for correlated time seriesDrausin Wulsin, Emily B. Fox, Brian Litt. 356-364 [doi]
• Planning by Prioritized Sweeping with Small BackupsHarm van Seijen, Rich Sutton. 361-369 [doi]
• Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule dataJan-Willem van de Meent, Jonathan E. Bronson, Frank Wood, Ruben L. Gonzalez, Chris Wiggins. 361-369 [doi]
• Optimal rates for stochastic convex optimization under Tsybakov noise conditionAaditya Ramdas, Aarti Singh. 365-373 [doi]
• Solving Continuous POMDPs: Value Iteration with Incremental Learning of an Efficient Space RepresentationSebastian Brechtel, Tobias Gindele, Rüdiger Dillmann. 370-378 [doi]
• Activized Learning with Uniform Classification NoiseLiu Yang, Steve Hanneke. 370-378 [doi]
• A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel LearningArash Afkanpour, András György, Csaba Szepesvári, Michael Bowling. 374-382 [doi]
• Learning Heteroscedastic Models by Convex Programming under Group SparsityArnak S. Dalalyan, Mohamed Hebiri, Katia Meziani, Joseph Salmon. 379-387 [doi]
• Noisy and Missing Data Regression: Distribution-Oblivious Support RecoveryYudong Chen, Constantine Caramanis. 383-391 [doi]
• Covariate Shift in Hilbert Space: A Solution via Sorrogate KernelsKai Zhang, Vincent Wenchen Zheng, Qiaojun Wang, James Tin-Yau Kwok, Qiang Yang, Ivan Marsic. 388-395 [doi]
• Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier MethodTaiji Suzuki. 392-400 [doi]
• A Local Algorithm for Finding Well-Connected ClustersZeyuan Allen Zhu, Silvio Lattanzi, Vahab S. Mirrokni. 396-404 [doi]
• A New Frontier of Kernel Design for Structured DataKilho Shin. 401-409 [doi]
• Efficient Multi-label Classification with Many LabelsWei Bi, James Tin-Yau Kwok. 405-413 [doi]
• Learning with Marginalized Corrupted FeaturesLaurens van der Maaten, Minmin Chen, Stephen Tyree, Kilian Q. Weinberger. 410-418 [doi]
• Spectral Compressed Sensing via Structured Matrix CompletionYuxin Chen, Yuejie Chi. 414-422 [doi]
• Approximation properties of DBNs with binary hidden units and real-valued visible unitsOswin Krause, Asja Fischer, Tobias Glasmachers, Christian Igel. 419-426 [doi]
• Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian ModelMing Yang, Yingming Li, Zhongfei Zhang. 423-431 [doi]
• Revisiting Frank-Wolfe: Projection-Free Sparse Convex OptimizationMartin Jaggi. 427-435 [doi]
• Simple Sparsification Improves Sparse Denoising Autoencoders in Denoising Highly Corrupted ImagesKyungHyun Cho. 432-440 [doi]
• General Functional Matrix Factorization Using Gradient BoostingTianQi Chen, Hang Li, Qiang Yang 0001, Yong Yu. 436-444 [doi]
• On the Generalization Ability of Online Learning Algorithms for Pairwise Loss FunctionsPurushottam Kar, Bharath K. Sriperumbudur, Prateek Jain 0002, Harish Karnick. 441-449 [doi]
• Iterative Learning and Denoising in Convolutional Neural Associative MemoriesAmin Karbasi, Amir Hesam Salavati, Amin Shokrollahi. 445-453 [doi]
• Non-Linear Stationary Subspace Analysis with Application to Video ClassificationMahsa Baktashmotlagh, Mehrtash Tafazzoli Harandi, Abbas Bigdeli, Brian C. Lovell, Mathieu Salzmann. 450-458 [doi]
• Scaling Multidimensional Gaussian Processes using Projected Additive ApproximationsElad Gilboa, Yunus Saatçi, John P. Cunningham, Elad Gilboa. 454-461 [doi]
• Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon EntropyJean Honorio, Tommi Jaakkola. 459-467 [doi]
• Active Learning for Multi-Objective OptimizationMarcela Zuluaga, Guillaume Sergent, Andreas Krause, Markus Püschel. 462-470 [doi]
• That was fast! Speeding up NN search of high dimensional distributionsEmanuele Coviello, Adeel Mumtaz, Antoni B. Chan, Gert R. G. Lanckriet. 468-476 [doi]
• A Generalized Kernel Approach to Structured Output LearningHachem Kadri, Mohammad Ghavamzadeh, Philippe Preux. 471-479 [doi]
• Entropic Affinities: Properties and Efficient Numerical ComputationMax Vladymyrov, Miguel Á. Carreira-Perpiñán. 477-485 [doi]
• Efficient Active Learning of Halfspaces: an Aggressive ApproachAlon Gonen, Sivan Sabato, Shai Shalev-Shwartz. 480-488 [doi]
• Local Deep Kernel Learning for Efficient Non-linear SVM PredictionCijo Jose, Prasoon Goyal, Parv Aggrwal, Manik Varma. 486-494 [doi]
• Enhanced statistical rankings via targeted data collectionBraxton Osting, Christoph Brune, Stanley Osher. 489-497 [doi]
• Temporal Difference Methods for the Variance of the Reward To GoAviv Tamar, Dotan Di Castro, Shie Mannor. 495-503 [doi]
• Online Feature Selection for Model-based Reinforcement LearningTrung Thanh Nguyen 0005, Zhuoru Li, Tomi Silander, Tze-Yun Leong. 498-506 [doi]
• \(\propto\)SVM for Learning with Label ProportionsFelix X. Yu, Dong Liu, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang. 504-512 [doi]
• ELLA: An Efficient Lifelong Learning AlgorithmPaul Ruvolo, Eric Eaton. 507-515 [doi]
• Parameter Learning and Convergent Inference for Dense Random FieldsPhilipp Kraehenbuehl, Vladlen Koltun. 513-521 [doi]
• A Structural SVM Based Approach for Optimizing Partial AUCHarikrishna Narasimhan, Shivani Agarwal. 516-524 [doi]
• Loss-Proportional Subsampling for Subsequent ERMPaul Mineiro, Nikos Karampatziakis. 522-530 [doi]
• Convex Relaxations for Learning Bounded-Treewidth Decomposable GraphsK. S. Sesh Kumar, Francis Bach. 525-533 [doi]
• Scalable Simple Random Sampling and Stratified SamplingXiangrui Meng. 531-539 [doi]
• Adaptive Task Assignment for Crowdsourced ClassificationChien-Ju Ho, Shahin Jabbari, Jennifer Wortman Vaughan. 534-542 [doi]
• Riemannian Similarity LearningLi Cheng. 540-548 [doi]
• Optimal Regret Bounds for Selecting the State Representation in Reinforcement LearningOdalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko. 543-551 [doi]
• On Compact Codes for Spatially Pooled FeaturesYangqing Jia, Oriol Vinyals, Trevor Darrell. 549-557 [doi]
• Better Mixing via Deep RepresentationsYoshua Bengio, Grégoire Mesnil, Yann Dauphin, Salah Rifai. 552-560 [doi]
• Dynamic Covariance Models for Multivariate Financial Time SeriesYue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani. 558-566 [doi]
• Online Latent Dirichlet Allocation with Infinite VocabularyKe Zhai, Jordan L. Boyd-Graber. 561-569 [doi]
• Revisiting the Nystrom method for improved large-scale machine learningAlex Gittens, Michael W. Mahoney. 567-575 [doi]
• Characterizing the Representer TheoremYaoliang Yu, Hao Cheng, Dale Schuurmans, Csaba Szepesvári. 570-578 [doi]
• Infinite Positive Semidefinite Tensor Factorization for Source Separation of Mixture SignalsKazuyoshi Yoshii, Ryota Tomioka, Daichi Mochihashi, Masataka Goto. 576-584 [doi]
• Dynamical Models and tracking regret in online convex programmingEric C. Hall, Rebecca Willett. 579-587 [doi]
• A Unified Robust Regression Model for Lasso-like AlgorithmsWenzhuo Yang, Huan Xu. 585-593 [doi]
• Large-Scale Bandit Problems and KWIK LearningJacob Abernethy, Kareem Amin, Michael Kearns, Moez Draief. 588-596 [doi]
• Quickly Boosting Decision Trees - Pruning Underachieving Features EarlyRon Appel, Thomas Fuchs, Piotr Dollár, Pietro Perona. 594-602 [doi]
• Vanishing Component AnalysisRoi Livni, David Lehavi, Sagi Schein, Hila Nachlieli, Shai Shalev-Shwartz, Amir Globerson. 597-605 [doi]
• On the Statistical Consistency of Algorithms for Binary Classification under Class ImbalanceAditya Menon, Harikrishna Narasimhan, Shivani Agarwal, Sanjay Chawla. 603-611 [doi]
• Learning an Internal Dynamics Model from Control DemonstrationMatthew Golub, Steven Chase, Byron Yu. 606-614 [doi]
• Topic Model Diagnostics: Assessing Domain Relevance via Topical AlignmentJason Chuang, Sonal Gupta, Christopher Manning, Jeffrey Heer. 612-620 [doi]
• Robust Structural Metric LearningDaryl Lim, Gert R. G. Lanckriet, Brian McFee. 615-623 [doi]
• Online Kernel Learning with a Near Optimal Sparsity BoundLijun Zhang, Jinfeng Yi, Rong Jin, Ming Lin, Xiaofei He. 621-629 [doi]
• Constrained fractional set programs and their application in local clustering and community detectionThomas Bühler, Syama Sundar Rangapuram, Simon Setzer, Matthias Hein. 624-632 [doi]
• Spectral Learning of Hidden Markov Models from Dynamic and Static DataTzu-Kuo Huang, Jeff G. Schneider. 630-638 [doi]
• Efficient Semi-supervised and Active Learning of DisjunctionsNina Balcan, Christopher Berlind, Steven Ehrlich, Yingyu Liang. 633-641 [doi]
• Analogy-preserving Semantic Embedding for Visual Object CategorizationSung Ju Hwang, Kristen Grauman, Fei Sha. 639-647 [doi]
• Convex Adversarial Collective ClassificationMohamadAli Torkamani, Daniel Lowd. 642-650 [doi]
• Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel trainingMichael Izbicki. 648-656 [doi]
• Rounding Methods for Discrete Linear ClassificationYann Chevaleyre, Frédéerick Koriche, Jean-Daniel Zucker. 651-659 [doi]
• Factorial Multi-Task Learning : A Bayesian Nonparametric ApproachSunil Kumar Gupta, Dinh Q. Phung, Svetha Venkatesh. 657-665 [doi]
• Modeling Information Propagation with Survival TheoryManuel Gomez-Rodriguez, Jure Leskovec, Bernhard Schölkopf. 666-674 [doi]
• Better Rates for Any Adversarial Deterministic MDPOfer Dekel, Elad Hazan. 675-683 [doi]
• ABC Reinforcement LearningChristos Dimitrakakis, Nikolaos Tziortziotis. 684-692 [doi]
• Sharp Generalization Error Bounds for Randomly-projected ClassifiersRobert J. Durrant, Ata Kaban. 693-701 [doi]
• On learning parametric-output HMMsAryeh Kontorovich, Boaz Nadler, Roi Weiss. 702-710 [doi]
• LDA Topic Model with Soft Assignment of Descriptors to WordsDaphna Weinshall, Gal Levi, Dmitri Hanukaev. 711-719 [doi]
• On autoencoder scoringHanna Kamyshanska, Roland Memisevic. 720-728 [doi]
• Infinite Markov-Switching Maximum Entropy Discrimination MachinesSotirios Chatzis. 729-737 [doi]
• A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear ClassifiersPascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant. 738-746 [doi]
• Sparse PCA through Low-rank ApproximationsDimitris S. Papailiopoulos, Alexandros G. Dimakis, Stavros Korokythakis. 747-755 [doi]
• Computation-Risk Tradeoffs for Covariance-Thresholded RegressionDinah Shender, John D. Lafferty. 756-764 [doi]
• Exact Rule Learning via Boolean Compressed SensingDmitry M. Malioutov, Kush Varshney. 765-773 [doi]
• Robust Sparse Regression under Adversarial CorruptionYudong Chen, Constantine Caramanis, Shie Mannor. 774-782 [doi]
• Optimization with First-Order Surrogate FunctionsJulien Mairal. 783-791 [doi]
• Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and AnticipationHema Swetha Koppula, Ashutosh Saxena. 792-800 [doi]
• Consistency versus Realizable H-Consistency for Multiclass ClassificationPhilip M. Long, Rocco A. Servedio. 801-809 [doi]
• Feature Multi-Selection among Subjective FeaturesSivan Sabato, Adam Kalai. 810-818 [doi]
• Domain Adaptation under Target and Conditional ShiftKun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang. 819-827 [doi]
• Collective Stability in Structured Prediction: Generalization from One ExampleBen London, Bert Huang, Ben Taskar, Lise Getoor. 828-836 [doi]
• Stable Coactive Learning via PerturbationKarthik Raman, Thorsten Joachims, Pannaga Shivaswamy, Tobias Schnabel. 837-845 [doi]
• Max-Margin Multiple-Instance Dictionary LearningXinggang Wang, Baoyuan Wang, Xiang Bai, Wenyu Liu, Zhuowen Tu. 846-854 [doi]
• Fast Semidifferential-based Submodular Function OptimizationRishabh Iyer, Stefanie Jegelka, Jeff Bilmes. 855-863 [doi]
• Kernelized Bayesian Matrix FactorizationMehmet Gönen, Suleiman A. Khan, Samuel Kaski. 864-872 [doi]
• Learning the Structure of Sum-Product NetworksRobert Gens, Pedro Domingos. 873-880 [doi]
• Quantile Regression for Large-scale ApplicationsJiyan Yang, Xiangrui Meng, Michael W. Mahoney. 881-887 [doi]
• Robust Regression on MapReduceXiangrui Meng, Michael W. Mahoney. 888-896 [doi]
• Infinitesimal Annealing for Training Semi-Supervised Support Vector MachinesKohei Ogawa, Motoki Imamura, Ichiro Takeuchi, Masashi Sugiyama. 897-905 [doi]
• One-Pass AUC OptimizationWei Gao, Rong Jin, Shenghuo Zhu, Zhi-Hua Zhou. 906-914 [doi]
• Learning Convex QP Relaxations for Structured PredictionJeremy Jancsary, Sebastian Nowozin, Carsten Rother. 915-923 [doi]
• Concurrent Reinforcement Learning from Customer InteractionsDavid Silver, Leonard Newnham, David Barker, Suzanne Weller, Jason McFall. 924-932 [doi]
• Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base LearnerPeng Sun, Jie Zhou. 933-941 [doi]
• Stability and Hypothesis Transfer LearningIlja Kuzborskij, Francesco Orabona. 942-950 [doi]
• Fast Dual Variational Inference for Non-Conjugate Latent Gaussian ModelsMohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, Matthias Seeger. 951-959 [doi]
• Modeling Temporal Evolution and Multiscale Structure in NetworksTue Herlau, Morten Mørup, Mikkel N. Schmidt. 960-968 [doi]
• Dependent Normalized Random MeasuresChangyou Chen, Vinayak Rao, Wray L. Buntine, Yee Whye Teh. 969-977 [doi]
• Fast Max-Margin Matrix Factorization with Data AugmentationMinjie Xu, Jun Zhu, Bo Zhang. 978-986 [doi]
• Natural Image Bases to Represent Neuroimaging DataAshish Gupta, Murat Ayhan, Anthony Maida. 987-994 [doi]
• Breaking the Small Cluster Barrier of Graph ClusteringNir Ailon, Yudong Chen, Huan Xu. 995-1003 [doi]
• Approximate Inference in Collective Graphical ModelsDaniel Sheldon, Tao Sun, Akshat Kumar, Thomas G. Dietterich. 1004-1012 [doi]
• Scaling the Indian Buffet Process via Submodular MaximizationColorado Reed, Zoubin Ghahramani. 1013-1021 [doi]
• Mini-Batch Primal and Dual Methods for SVMsMartin Takác, Avleen Singh Bijral, Peter Richtárik, Nati Srebro. 1022-1030 [doi]
• The lasso, persistence, and cross-validationDarren Homrighausen, Daniel McDonald. 1031-1039 [doi]
• Spectral Experts for Estimating Mixtures of Linear RegressionsArun Tejasvi Chaganty, Percy Liang. 1040-1048 [doi]
• Distribution to Distribution RegressionJunier Oliva, Barnabás Póczos, Jeff G. Schneider. 1049-1057 [doi]
• Regularization of Neural Networks using DropConnectLi Wan, Matthew D. Zeiler, Sixin Zhang, Yann LeCun, Rob Fergus. 1058-1066 [doi]
• Gaussian Process Kernels for Pattern Discovery and ExtrapolationAndrew Wilson, Ryan Adams. 1067-1075 [doi]
• Anytime Representation LearningZhixiang Eddie Xu, Matt J. Kusner, Gao Huang, Kilian Q. Weinberger. 1076-1084 [doi]
• Algorithms for Direct 0-1 Loss Optimization in Binary ClassificationTan Nguyen, Scott Sanner. 1085-1093 [doi]
• Top-k Selection based on Adaptive Sampling of Noisy PreferencesRóbert Busa-Fekete, Balázs Szörényi, Weiwei Cheng, Paul Weng, Eyke Hüllermeier. 1094-1102 [doi]
• The Extended Parameter FilterYusuf Erol, Lei Li, Bharath Ramsundar, Stuart J. Russell. 1103-1111 [doi]
• Exploiting Ontology Structures and Unlabeled Data for LearningNina Balcan, Avrim Blum, Yishay Mansour. 1112-1120 [doi]
• O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex FunctionsLijun Zhang, Tianbao Yang, Rong Jin, Xiaofei He. 1121-1129 [doi]
• Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss MinimizationKrzysztof Dembczynski, Arkadiusz Jachnik, Wojciech Kotlowski, Willem Waegeman, Eyke Hüllermeier. 1130-1138 [doi]
• On the importance of initialization and momentum in deep learningIlya Sutskever, James Martens, George E. Dahl, Geoffrey E. Hinton. 1139-1147 [doi]
• A non-IID Framework for Collaborative Filtering with Restricted Boltzmann MachinesKostadin Georgiev, Preslav Nakov. 1148-1156 [doi]
• Intersecting singularities for multi-structured estimationEmile Richard, Francis R. Bach, Jean-Philippe Vert. 1157-1165 [doi]
• Structure Discovery in Nonparametric Regression through Compositional Kernel SearchDavid K. Duvenaud, James Robert Lloyd, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani. 1166-1174 [doi]
• Copy or Coincidence? A Model for Detecting Social Influence and Duplication EventsLisa Friedland, David Jensen, Michael Lavine. 1175-1183 [doi]
• Smooth OperatorsSteffen Grünewälder, Arthur Gretton, John Shawe-Taylor. 1184-1192 [doi]
• The Cross-Entropy Method Optimizes for QuantilesSergiu Goschin, Ari Weinstein, Michael L. Littman. 1193-1201 [doi]
• Topic Discovery through Data Dependent and Random ProjectionsWeicong Ding, Mohammad Hossein Rohban, Prakash Ishwar, Venkatesh Saligrama. 1202-1210 [doi]
• Bayesian Learning of Recursively Factored EnvironmentsMarc G. Bellemare, Joel Veness, Michael Bowling. 1211-1219 [doi]
• Selective sampling algorithms for cost-sensitive multiclass predictionAlekh Agarwal. 1220-1228 [doi]
• The Bigraphical LassoAlfredo A. Kalaitzis, John D. Lafferty, Neil D. Lawrence, Shuheng Zhou. 1229-1237 [doi]
• Almost Optimal Exploration in Multi-Armed BanditsZohar Karnin, Tomer Koren, Oren Somekh. 1238-1246 [doi]
• Deep Canonical Correlation AnalysisGalen Andrew, Raman Arora, Jeff Bilmes, Karen Livescu. 1247-1255 [doi]
• Consistency of Online Random ForestsMisha Denil, David Matheson, Nando de Freitas. 1256-1264 [doi]
• Sparse Gaussian Conditional Random Fields: Algorithms, Theory, and Application to Energy ForecastingMatt Wytock, J. Zico Kolter. 1265-1273 [doi]
• Fast Image TaggingMinmin Chen, Alice Zheng, Kilian Q. Weinberger. 1274-1282 [doi]
• Expensive Function Optimization with Stochastic Binary OutcomesMatthew Tesch, Jeff G. Schneider, Howie Choset. 1283-1291 [doi]
• Multiple-source cross-validationKrzysztof Geras, Charles A. Sutton. 1292-1300 [doi]
• Learning Triggering Kernels for Multi-dimensional Hawkes ProcessesKe Zhou, Hongyuan Zha, Le Song. 1301-1309 [doi]
• On the difficulty of training recurrent neural networksRazvan Pascanu, Tomas Mikolov, Yoshua Bengio. 1310-1318 [doi]
• Maxout NetworksIan J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron C. Courville, Yoshua Bengio. 1319-1327 [doi]
• Predictable Dual-View HashingMohammad Rastegari, Jonghyun Choi, Shobeir Fakhraei, Daume Hal, Larry S. Davis. 1328-1336 [doi]
• Deep learning with COTS HPC systemsAdam Coates, Brody Huval, Tao Wang, David J. Wu, Bryan C. Catanzaro, Andrew Y. Ng. 1337-1345 [doi]
• Nonparametric Mixture of Gaussian Processes with ConstraintsJames Ross, Jennifer Dy. 1346-1354 [doi]
• Scale Invariant Conditional Dependence MeasuresSashank J. Reddi, Barnabás Póczos. 1355-1363 [doi]
• Learning Policies for Contextual Submodular PredictionStéphane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, Drew Bagnell. 1364-1372 [doi]
• Manifold Preserving Hierarchical Topic Models for Quantization and ApproximationMinje Kim, Paris Smaragdis. 1373-1381 [doi]
• Safe Screening of Non-Support Vectors in Pathwise SVM ComputationKohei Ogawa, Yoshiki Suzuki, Ichiro Takeuchi. 1382-1390 [doi]
• Cost-sensitive Multiclass Classification Risk BoundsBernardo Avila Pires, Csaba Szepesvári, Mohammad Ghavamzadeh. 1391-1399 [doi]
• Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix CompletionJinfeng Yi, Lijun Zhang, Rong Jin, Qi Qian, Anil K. Jain. 1400-1408 [doi]
• Learning the beta-Divergence in Tweedie Compound Poisson Matrix Factorization ModelsUmut Simsekli, Ali Taylan Cemgil, Yusuf Kenan Yilmaz. 1409-1417 [doi]
• Fast algorithms for sparse principal component analysis based on Rayleigh quotient iterationVolodymyr Kuleshov. 1418-1425 [doi]
• Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document ModelingAmr Ahmed, Liangjie Hong, Alexander J. Smola. 1426-1434 [doi]
• Tree-Independent Dual-Tree AlgorithmsRyan R. Curtin, William B. March, Parikshit Ram, David V. Anderson, Alexander G. Gray, Charles L. Isbell. 1435-1443 [doi]
• Multilinear Multitask LearningBernardino Romera-Paredes, Hane Aung, Nadia Bianchi-Berthouze, Massimiliano Pontil. 1444-1452 [doi]
• Online Learning under Delayed FeedbackPooria Joulani, András György, Csaba Szepesvári. 1453-1461 [doi]
• Adaptive Hamiltonian and Riemann Manifold Monte CarloZiyu Wang, Shakir Mohamed, Nando de Freitas. 1462-1470 [doi]
• Coco-Q: Learning in Stochastic Games with Side PaymentsEric Sodomka, Elizabeth Hilliard, Michael L. Littman, Amy Greenwald. 1471-1479 [doi]
• On A Nonlinear Generalization of Sparse Coding and Dictionary LearningJeffrey Ho, Yuchen Xie, Baba C. Vemuri. 1480-1488 [doi]
• Estimation of Causal Peer Influence EffectsPanos Toulis, Edward Kao. 1489-1497 [doi]