Table of Contents

• A Discriminative Latent Variable Model for Online ClusteringRajhans Samdani, Kai-Wei Chang, Dan Roth. 1-9 [doi]
• An Information Geometry of Statistical Manifold LearningKe Sun, Stéphane Marchand-Maillet. 1-9 [doi]
• Kernel Mean Estimation and Stein EffectKrikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf. 10-18 [doi]
• Relative Upper Confidence Bound for the K-Armed Dueling Bandit ProblemMasrour Zoghi, Shimon Whiteson, Rémi Munos, Maarten de Rijke. 10-18 [doi]
• Demystifying Information-Theoretic ClusteringGreg Ver Steeg, Aram Galstyan, Fei Sha, Simon Dedeo. 19-27 [doi]
• Compact Random Feature MapsRaffay Hamid, Ying Xiao, Alex Gittens, Dennis DeCoste. 19-27 [doi]
• Covering Number for Efficient Heuristic-based POMDP PlanningZongzhang Zhang, David Hsu, Wee Sun Lee. 28-36 [doi]
• Concentration in unbounded metric spaces and algorithmic stabilityAryeh Kontorovich. 28-36 [doi]
• The Coherent Loss Function for ClassificationWenzhuo Yang, Melvyn Sim, Huan Xu. 37-45 [doi]
• Heavy-tailed regression with a generalized median-of-meansDaniel Hsu, Sivan Sabato. 37-45 [doi]
• Spectral Bandits for Smooth Graph FunctionsMichal Valko, Rémi Munos, Branislav Kveton, Tomás Kocák. 46-54 [doi]
• Fast Stochastic Alternating Direction Method of MultipliersWenliang Zhong, James Tin-Yau Kwok. 46-54 [doi]
• Active Detection via Adaptive SubmodularityYuxin Chen, Hiroaki Shioi, Cesar Fuentes Montesinos, Lian Pin Koh, Serge Wich, Andreas Krause. 55-63 [doi]
• Robust Principal Component Analysis with Complex NoiseQian Zhao, Deyu Meng, ZongBen Xu, Wangmeng Zuo, Lei Zhang. 55-63 [doi]
• Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss MinimizationShai Shalev-Shwartz, Tong Zhang 0001. 64-72 [doi]
• Scalable Semidefinite Relaxation for Maximum A Posterior EstimationQi-Xing Huang, Yuxin Chen, Leonidas J. Guibas. 64-72 [doi]
• An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse OptimizationQihang Lin, Lin Xiao. 73-81 [doi]
• Square Deal: Lower Bounds and Improved Relaxations for Tensor RecoveryCun Mu, Bo Huang, John Wright, Donald Goldfarb. 73-81 [doi]
• Recurrent Convolutional Neural Networks for Scene LabelingPedro H. O. Pinheiro, Ronan Collobert. 82-90 [doi]
• Automated inference of point of view from user interactions in collective intelligence venuesSanmay Das, Allen Lavoie. 82-90 [doi]
• A Statistical Perspective on Algorithmic LeveragingPing Ma, Michael W. Mahoney, Bin Yu. 91-99 [doi]
• Rank-One Matrix Pursuit for Matrix CompletionZheng Wang, Ming-Jun Lai, Zhaosong Lu, Wei Fan, Hasan Davulcu, Jieping Ye. 91-99 [doi]
• Thompson Sampling for Complex Online ProblemsAditya Gopalan, Shie Mannor, Yishay Mansour. 100-108 [doi]
• Near-Optimal Joint Object Matching via Convex RelaxationYuxin Chen, Leonidas J. Guibas, Qi-Xing Huang. 100-108 [doi]
• Boosting multi-step autoregressive forecastsSouhaib Ben Taieb, Rob J. Hyndman. 109-117 [doi]
• Convex Total Least SquaresDmitry Malioutov, Nikolai Slavov. 109-117 [doi]
• On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature SelectionPratik Jawanpuria, Manik Varma, J. Saketha Nath. 118-126 [doi]
• A Statistical Convergence Perspective of Algorithms for Rank Aggregation from Pairwise DataArun Rajkumar, Shivani Agarwal. 118-126 [doi]
• Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer IterationsTimothy Mann, Shie Mannor. 127-135 [doi]
• Gradient Hard Thresholding Pursuit for Sparsity-Constrained OptimizationXiaotong Yuan, Ping Li, Tong Zhang. 127-135 [doi]
• Latent BanditsOdalric-Ambrym Maillard, Shie Mannor. 136-144 [doi]
• A Unified Framework for Consistency of Regularized Loss MinimizersJean Honorio, Tommi Jaakkola. 136-144 [doi]
• Geodesic Distance Function Learning via Heat Flow on Vector FieldsBinbin Lin, Ji Yang, Xiaofei He, Jieping Ye. 145-153 [doi]
• Fast Allocation of Gaussian Process ExpertsTrung Nguyen, Edwin V. Bonilla. 145-153 [doi]
• Von Mises-Fisher Clustering ModelsSiddharth Gopal, Yiming Yang. 154-162 [doi]
• Near-Optimally Teaching the Crowd to ClassifyAdish Singla, Ilija Bogunovic, Gábor Bartók, Amin Karbasi, Andreas Krause. 154-162 [doi]
• On the convergence of no-regret learning in selfish routingWalid Krichene, Benjamin Drighès, Alexandre M. Bayen. 163-171 [doi]
• Convergence rates for persistence diagram estimation in Topological Data AnalysisFrédéric Chazal, Marc Glisse, Catherine Labruère, Bertrand Michel. 163-171 [doi]
• Improving offline evaluation of contextual bandit algorithms via bootstrapping techniquesJérémie Mary, Philippe Preux, Olivier Nicol. 172-180 [doi]
• Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUsFabian Gieseke, Justin Heinermann, Cosmin E. Oancea, Christian Igel. 172-180 [doi]
• Austerity in MCMC Land: Cutting the Metropolis-Hastings BudgetAnoop Korattikara Balan, Yutian Chen, Max Welling. 181-189 [doi]
• Scaling Up Robust MDPs using Function ApproximationAviv Tamar, Shie Mannor, Huan Xu. 181-189 [doi]
• Understanding the Limiting Factors of Topic Modeling via Posterior Contraction AnalysisJian Tang, Zhaoshi Meng, XuanLong Nguyen, Qiaozhu Mei, Ming Zhang. 190-198 [doi]
• Marginal Structured SVM with Hidden VariablesWei Ping, Qiang Liu, Alexander T. Ihler. 190-198 [doi]
• Linear and Parallel Learning of Markov Random FieldsYariv Dror Mizrahi, Misha Denil, Nando de Freitas. 199-207 [doi]
• The Inverse Regression Topic ModelMaxim Rabinovich, David M. Blei. 199-207 [doi]
• Pitfalls in the use of Parallel Inference for the Dirichlet ProcessYarin Gal, Zoubin Ghahramani. 208-216 [doi]
• A Consistent Histogram Estimator for Exchangeable Graph ModelsStanley H. Chan, Edoardo Airoldi. 208-216 [doi]
• Latent Variable Copula Inference for Bundle Pricing from Retail Transaction DataBenjamin Letham, Wei Sun, Anshul Sheopuri. 217-225 [doi]
• Optimal PAC Multiple Arm Identification with Applications to CrowdsourcingYuan Zhou, Xi Chen, Jian Li. 217-225 [doi]
• Deep Generative Stochastic Networks Trainable by BackpropYoshua Bengio, Eric Laufer, Guillaume Alain, Jason Yosinski. 226-234 [doi]
• Towards Minimax Online Learning with Unknown Time HorizonHaipeng Luo, Robert E. Schapire. 226-234 [doi]
• A Highly Scalable Parallel Algorithm for Isotropic Total Variation ModelsJie Wang, Qingyang Li, Sen Yang, Wei Fan, Peter Wonka, Jieping Ye. 235-243 [doi]
• Factorized Point Process Intensities: A Spatial Analysis of Professional BasketballAndrew Miller, Luke Bornn, Ryan Adams, Kirk Goldsberry. 235-243 [doi]
• Margins, Kernels and Non-linear Smoothed PerceptronsAaditya Ramdas, Javier Peña. 244-252 [doi]
• Statistical-Computational Phase Transitions in Planted Models: The High-Dimensional SettingYudong Chen, Jiaming Xu. 244-252 [doi]
• Gaussian Process Optimization with Mutual InformationEmile Contal, Vianney Perchet, Nicolas Vayatis. 253-261 [doi]
• Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian ModelsShike Mei, Jun Zhu, Jerry Zhu. 253-261 [doi]
• Learning Theory and Algorithms for revenue optimization in second price auctions with reserveMehryar Mohri, Andres Muñoz Medina. 262-270 [doi]
• Aggregating Ordinal Labels from Crowds by Minimax Conditional EntropyDengyong Zhou, Qiang Liu, John C. Platt, Christopher Meek. 262-270 [doi]
• Exchangeable Variable ModelsMathias Niepert, Pedro Domingos. 271-279 [doi]
• Low-density Parity Constraints for Hashing-Based Discrete IntegrationStefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman. 271-279 [doi]
• Clustering in the Presence of Background NoiseShai Ben-David, Nika Haghtalab. 280-288 [doi]
• Prediction with Limited Advice and Multiarmed Bandits with Paid ObservationsYevgeny Seldin, Peter L. Bartlett, Koby Crammer, Yasin Abbasi-Yadkori. 280-287 [doi]
• Bayesian Nonparametric Multilevel Clustering with Group-Level ContextsTien-Vu Nguyen, Dinh Quoc Phung, XuanLong Nguyen, Svetha Venkatesh, Hung Bui. 288-296 [doi]
• Safe Screening with Variational Inequalities and Its Application to LassoJun Liu, Zheng Zhao, Jie Wang, Jieping Ye. 289-297 [doi]
• Large-Margin Metric Learning for Constrained Partitioning ProblemsRémi Lajugie, Francis Bach, Sylvain Arlot. 297-305 [doi]
• Learning the Consistent Behavior of Common Users for Target Node Prediction across Social NetworksShan-Hung Wu, Hao-Heng Chien, Kuan-Hua Lin, Philip Yu. 298-306 [doi]
• Wasserstein Propagation for Semi-Supervised LearningJustin Solomon, Raif M. Rustamov, Leonidas J. Guibas, Adrian Butscher. 306-314 [doi]
• Signal recovery from Pooling RepresentationsJoan Bruna Estrach, Arthur Szlam, Yann LeCun. 307-315 [doi]
• Max-Margin Infinite Hidden Markov ModelsAonan Zhang, Jun Zhu, Bo Zhang. 315-323 [doi]
• PAC-inspired Option Discovery in Lifelong Reinforcement LearningEmma Brunskill, Lihong Li. 316-324 [doi]
• Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence FunctionYong Liu, Shali Jiang, Shizhong Liao. 324-332 [doi]
• Multi-label Classification via Feature-aware Implicit Label Space EncodingZijia Lin, Guiguang Ding, Mingqing Hu, Jianmin Wang 0001. 325-333 [doi]
• Generalized Exponential Concentration Inequality for Renyi Divergence EstimationShashank Singh, Barnabás Póczos. 333-341 [doi]
• Scalable Gaussian Process Structured Prediction for Grid Factor Graph ApplicationsSebastien Bratieres, Novi Quadrianto, Sebastian Nowozin, Zoubin Ghahramani. 334-342 [doi]
• Boosting with Online Binary Learners for the Multiclass Bandit ProblemShang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu. 342-350 [doi]
• Anomaly Ranking as Supervised Bipartite RankingStéphan Clémençon, Sylvain Robbiano. 343-351 [doi]
• Optimal Budget Allocation: Theoretical Guarantee and Efficient AlgorithmTasuku Soma, Naonori Kakimura, Kazuhiro Inaba, Ken-ichi Kawarabayashi. 351-359 [doi]
• Hierarchical Quasi-Clustering Methods for Asymmetric NetworksGunnar E. Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra. 352-360 [doi]
• Computing Parametric Ranking Models via Rank-BreakingHossein Azari Soufiani, David C. Parkes, Lirong Xia. 360-368 [doi]
• Rectangular Tiling ProcessMasahiro Nakano, Katsuhiko Ishiguro, Akisato Kimura, Takeshi Yamada, Naonori Ueda. 361-369 [doi]
• Tracking Adversarial TargetsYasin Abbasi-Yadkori, Peter L. Bartlett, Varun Kanade. 369-377 [doi]
• Two-Stage Metric LearningJun Wang 0017, Ke Sun, Fei Sha, Stéphane Marchand-Maillet, Alexandros Kalousis. 370-378 [doi]
• Online Bayesian Passive-Aggressive LearningTianlin Shi, Jun Zhu. 378-386 [doi]
• Stochastic Inference for Scalable Probabilistic Modeling of Binary MatricesJosé Miguel Hernández-Lobato, Neil Houlsby, Zoubin Ghahramani. 379-387 [doi]
• Deterministic Policy Gradient AlgorithmsDavid Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin Riedmiller. 387-395 [doi]
• Elementary Estimators for High-Dimensional Linear RegressionEunho Yang, Aurelie C. Lozano, Pradeep D. Ravikumar. 388-396 [doi]
• Modeling Correlated Arrival Events with Latent Semi-Markov ProcessesWenzhao Lian, Vinayak Rao, Brian Eriksson, Lawrence Carin. 396-404 [doi]
• Elementary Estimators for Sparse Covariance Matrices and other Structured MomentsEunho Yang, Aurelie C. Lozano, Pradeep D. Ravikumar. 397-405 [doi]
• Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approachRémi Bardenet, Arnaud Doucet, Christopher C. Holmes. 405-413 [doi]
• Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness ProbabilisticallyYuan Fang, Kevin Chen-Chuan Chang, Hady Wirawan Lauw. 406-414 [doi]
• Diagnosis determination: decision trees optimizing simultaneously worst and expected testing costFerdinando Cicalese, Eduardo Sany Laber, Aline Medeiros Saettler. 414-422 [doi]
• Bayesian Max-margin Multi-Task Learning with Data AugmentationChengtao Li, Jun Zhu, Jianfei Chen 0001. 415-423 [doi]
• Condensed Filter Tree for Cost-Sensitive Multi-Label ClassificationChun-Liang Li, Hsuan-Tien Lin. 423-431 [doi]
• Sparse Reinforcement Learning via Convex OptimizationZhiwei Qin, Weichang Li, Firdaus Janoos. 424-432 [doi]
• On Measure Concentration of Random Maximum A-Posteriori PerturbationsFrancesco Orabona, Tamir Hazan, Anand D. Sarwate, Tommi Jaakkola. 432-440 [doi]
• Gaussian Process Classification and Active Learning with Multiple AnnotatorsFilipe Rodrigues, Francisco C. Pereira, Bernardete Ribeiro. 433-441 [doi]
• Bias in Natural Actor-Critic AlgorithmsPhilip Thomas. 441-448 [doi]
• Structured Prediction of Network ResponseHongYu Su, Aristides Gionis, Juho Rousu. 442-450 [doi]
• Dimension-free Concentration Bounds on Hankel Matrices for Spectral LearningFrançois Denis, Mattias Gybels, Amaury Habrard. 449-457 [doi]
• An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation AccuracyGavin Taylor, Connor Geer, David Piekut. 451-459 [doi]
• On Modelling Non-linear Topical DependenciesZhixing Li, Siqiang Wen, Juanzi Li, Peng Zhang, Jie Tang. 458-466 [doi]
• Optimization Equivalence of Divergences Improves Neighbor EmbeddingZhirong Yang, Jaakko Peltonen, Samuel Kaski. 460-468 [doi]
• A Deep and Tractable Density EstimatorBenigno Uria, Iain Murray, Hugo Larochelle. 467-475 [doi]
• An Asynchronous Parallel Stochastic Coordinate Descent AlgorithmJi Liu, Steve Wright, Christopher Ré, Victor Bittorf, Srikrishna Sridhar. 469-477 [doi]
• (Near) Dimension Independent Risk Bounds for Differentially Private LearningPrateek Jain 0002, Abhradeep Guha Thakurta. 476-484 [doi]
• Consistency of Causal Inference under the Additive Noise ModelSamory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf. 478-486 [doi]
• Quasi-Monte Carlo Feature Maps for Shift-Invariant KernelsJiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney. 485-493 [doi]
• Globally Convergent Parallel MAP LP Relaxation Solver using the Frank-Wolfe AlgorithmAlexander G. Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun. 487-495 [doi]
• Discriminative Features via Generalized EigenvectorsNikos Karampatziakis, Paul Mineiro. 494-502 [doi]
• Linear Programming for Large-Scale Markov Decision ProblemsAlan Malek, Yasin Abbasi-Yadkori, Peter L. Bartlett. 496-504 [doi]
• Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality ConstraintJi Liu 0002, Jieping Ye, Ryohei Fujimaki. 503-511 [doi]
• Linear Time Solver for Primal SVMFeiping Nie, Yizhen Huang, Heng Huang. 505-513 [doi]
• Online Learning in Markov Decision Processes with Changing Cost SequencesTravis Dick, András György, Csaba Szepesvári. 512-520 [doi]
• Memory (and Time) Efficient Sequential Monte CarloSeong-Hwan Jun, Alexandre Bouchard-Côté. 514-522 [doi]
• Unimodal Bandits: Regret Lower Bounds and Optimal AlgorithmsRichard Combes, Alexandre Proutiere. 521-529 [doi]
• Scaling SVM and Least Absolute Deviations via Exact Data ReductionJie Wang, Peter Wonka, Jieping Ye. 523-531 [doi]
• Maximum Mean Discrepancy for Class Ratio Estimation: Convergence Bounds and Kernel SelectionArun Iyer, Saketha Nath, Sunita Sarawagi. 530-538 [doi]
• Latent Semantic Representation Learning for Scene ClassificationXin Li, Yuhong Guo. 532-540 [doi]
• Asymptotically consistent estimation of the number of change points in highly dependent time seriesAzadeh Khaleghi, Daniil Ryabko. 539-547 [doi]
• Least Squares Revisited: Scalable Approaches for Multi-class PredictionAlekh Agarwal, Sham M. Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant. 541-549 [doi]
• Coordinate-descent for learning orthogonal matrices through Givens rotationsUri Shalit, Gal Chechik. 548-556 [doi]
• Local algorithms for interactive clusteringPranjal Awasthi, Maria-Florina Balcan, Konstantin Voevodski. 550-558 [doi]
• Densifying One Permutation Hashing via Rotation for Fast Near Neighbor SearchAnshumali Shrivastava, Ping Li 0001. 557-565 [doi]
• Model-Based Relational RL When Object Existence is Partially ObservableVien Ngo, Marc Toussaint. 559-567 [doi]
• A Divide-and-Conquer Solver for Kernel Support Vector MachinesCho-Jui Hsieh, Si Si, Inderjit S. Dhillon. 566-574 [doi]
• A new Q(lambda) with interim forward view and Monte Carlo equivalenceRichard S. Sutton, Ashique Rupam Mahmood, Doina Precup, Hado van Hasselt. 568-576 [doi]
• Nuclear Norm Minimization via Active Subspace SelectionCho-Jui Hsieh, Peder A. Olsen. 575-583 [doi]
• On Robustness and Regularization of Structural Support Vector MachinesMohamadAli Torkamani, Daniel Lowd. 577-585 [doi]
• Provable Bounds for Learning Some Deep RepresentationsSanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma. 584-592 [doi]
• Guess-Averse Loss Functions For Cost-Sensitive Multiclass BoostingOscar Beijbom, Mohammad J. Saberian, David J. Kriegman, Nuno Vasconcelos. 586-594 [doi]
• Large-scale Multi-label Learning with Missing LabelsHsiang-Fu Yu, Prateek Jain 0002, Purushottam Kar, Inderjit S. Dhillon. 593-601 [doi]
• Multimodal Neural Language ModelsRyan Kiros, Ruslan Salakhutdinov, Richard S. Zemel. 595-603 [doi]
• Learning Graphs with a Few HubsRashish Tandon, Pradeep D. Ravikumar. 602-610 [doi]
• Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methodsJascha Sohl-Dickstein, Ben Poole, Surya Ganguli. 604-612 [doi]
• Agnostic Bayesian Learning of EnsemblesAlexandre Lacoste, Mario Marchand, François Laviolette, Hugo Larochelle. 611-619 [doi]
• Alternating Minimization for Mixed Linear RegressionXinyang Yi, Constantine Caramanis, Sujay Sanghavi. 613-621 [doi]
• Towards an optimal stochastic alternating direction method of multipliersSamaneh Azadi, Suvrit Sra. 620-628 [doi]
• Stochastic Neighbor CompressionMatt J. Kusner, Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal. 622-630 [doi]
• Spherical Hamiltonian Monte Carlo for Constrained Target DistributionsShiwei Lan, Bo Zhou, Babak Shahbaba. 629-637 [doi]
• Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model MisspecificationJunfeng Wen, Chun-Nam Yu, Russell Greiner. 631-639 [doi]
• Efficient Continuous-Time Markov Chain EstimationMonir Hajiaghayi, Bonnie Kirkpatrick, Liangliang Wang, Alexandre Bouchard-Côté. 638-646 [doi]
• Nonparametric Estimation of Multi-View Latent Variable ModelsLe Song, Animashree Anandkumar, Bo Dai, Bo Xie. 640-648 [doi]
• DeCAF: A Deep Convolutional Activation Feature for Generic Visual RecognitionJeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell. 647-655 [doi]
• Structured Generative Models of Natural Source CodeChris J. Maddison, Daniel Tarlow. 649-657 [doi]
• Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of MultipliersDani Yogatama, Noah A. Smith. 656-664 [doi]
• A Single-Pass Algorithm for Efficiently Recovering Sparse Cluster Centers of High-dimensional DataJinfeng Yi, Lijun Zhang 0005, Jun Wang, Rong Jin, Anil K. Jain. 658-666 [doi]
• Narrowing the Gap: Random Forests In Theory and In PracticeMisha Denil, David Matheson, Nando de Freitas. 665-673 [doi]
• Statistical analysis of stochastic gradient methods for generalized linear modelsPanagiotis Toulis, Edoardo Airoldi, Jason Rennie. 667-675 [doi]
• Coherent Matrix CompletionYudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel Ward. 674-682 [doi]
• Coding for Random ProjectionsPing Li 0001, Michael Mitzenmacher, Anshumali Shrivastava. 676-684 [doi]
• Admixture of Poisson MRFs: A Topic Model with Word DependenciesDavid Inouye, Pradeep D. Ravikumar, Inderjit S. Dhillon. 683-691 [doi]
• Fast Computation of Wasserstein BarycentersMarco Cuturi, Arnaud Doucet. 685-693 [doi]
• True Online TD(lambda)Harm van Seijen, Richard S. Sutton. 692-700 [doi]
• Global graph kernels using geometric embeddingsFredrik Johansson, Vinay Jethava, Devdatt P. Dubhashi, Chiranjib Bhattacharyya. 694-702 [doi]
• Memory Efficient Kernel ApproximationSi Si, Cho-Jui Hsieh, Inderjit S. Dhillon. 701-709 [doi]
• Topic Modeling using Topics from Many Domains, Lifelong Learning and Big DataZhiyuan Chen, Bing Liu 0001. 703-711 [doi]
• Learning Sum-Product Networks with Direct and Indirect Variable InteractionsAmirmohammad Rooshenas, Daniel Lowd. 710-718 [doi]
• K-means recovers ICA filters when independent components are sparseAlon Vinnikov, Shai Shalev-Shwartz. 712-720 [doi]
• Hamiltonian Monte Carlo Without Detailed BalanceJascha Sohl-Dickstein, Mayur Mudigonda, Michael R. DeWeese. 719-726 [doi]
• Learning Mixtures of Linear ClassifiersYuekai Sun, Stratis Ioannidis, Andrea Montanari. 721-729 [doi]
• Filtering with Abstract ParticlesJacob Steinhardt, Percy Liang. 727-735 [doi]
• The Falling Factorial Basis and Its Statistical ApplicationsYu-Xiang Wang, Alexander J. Smola, Ryan J. Tibshirani. 730-738 [doi]
• Stochastic Dual Coordinate Ascent with Alternating Direction Method of MultipliersTaiji Suzuki. 736-744 [doi]
• Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian ProcessesTrong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan S. Kankanhalli. 739-747 [doi]
• Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure PredictionJian Zhou, Olga G. Troyanskaya. 745-753 [doi]
• A Unifying View of Representer TheoremsAndreas Argyriou, Francesco Dinuzzo. 748-756 [doi]
• An Efficient Approach for Assessing Hyperparameter ImportanceFrank Hutter, Holger Hoos, Kevin Leyton-Brown. 754-762 [doi]
• Online Clustering of BanditsClaudio Gentile, Shuai Li, Giovanni Zappella. 757-765 [doi]
• Cold-start Active Learning with Robust Ordinal Matrix FactorizationNeil Houlsby, José Miguel Hernández-Lobato, Zoubin Ghahramani. 766-774 [doi]
• Multivariate Maximal Correlation AnalysisHoang Vu Nguyen, Emmanuel Müller, Jilles Vreeken, Pavel Efros, Klemens Böhm. 775-783 [doi]
• Efficient Label PropagationYasuhiro Fujiwara, Go Irie. 784-792 [doi]
• Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding AlgorithmHadi Daneshmand, Manuel Gomez-Rodriguez, Le Song, Bernhard Schölkopf. 793-801 [doi]
• Coupled Group Lasso for Web-Scale CTR Prediction in Display AdvertisingLing Yan, Wu-Jun Li, Gui-Rong Xue, Dingyi Han. 802-810 [doi]
• Putting MRFs on a Tensor TrainAlexander Novikov, Anton Rodomanov, Anton Osokin, Dmitry Vetrov. 811-819 [doi]
• Efficient Algorithms for Robust One-bit Compressive SensingLijun Zhang 0005, Jinfeng Yi, Rong Jin. 820-828 [doi]
• Learning Complex Neural Network Policies with Trajectory OptimizationSergey Levine, Vladlen Koltun. 829-837 [doi]
• Composite Quantization for Approximate Nearest Neighbor SearchTing Zhang, Chao Du, Jingdong Wang. 838-846 [doi]
• Local Ordinal EmbeddingYoshikazu Terada, Ulrike von Luxburg. 847-855 [doi]
• Reducing Dueling Bandits to Cardinal BanditsNir Ailon, Zohar Shay Karnin, Thorsten Joachims. 856-864 [doi]
• Large-margin Weakly Supervised Dimensionality ReductionChang Xu, Dacheng Tao, Chao Xu, Yong Rui. 865-873 [doi]
• Joint Inference of Multiple Label Types in Large NetworksDeepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus A. Macskassy. 874-882 [doi]
• Hard-Margin Active Linear RegressionZohar Shay Karnin, Elad Hazan. 883-891 [doi]
• Maximum Margin Multiclass Nearest NeighborsAryeh Kontorovich, Roi Weiss. 892-900 [doi]
• Combinatorial Partial Monitoring Game with Linear Feedback and Its ApplicationsTian Lin, Bruno D. Abrahao, Robert D. Kleinberg, John Lui, Wei Chen. 901-909 [doi]
• Sparse meta-Gaussian information bottleneckMélanie Rey, Volker Roth, Thomas J. Fuchs. 910-918 [doi]
• Nonparametric Estimation of Renyi Divergence and FriendsAkshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman. 919-927 [doi]
• Robust Inverse Covariance Estimation under Noisy MeasurementsJun-Kun Wang, Shou-de Lin. 928-936 [doi]
• Bayesian Optimization with Inequality ConstraintsJacob R. Gardner, Matt J. Kusner, Zhixiang Eddie Xu, Kilian Q. Weinberger, John Cunningham. 937-945 [doi]
• Circulant Binary EmbeddingFelix X. Yu, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang. 946-954 [doi]
• Multiple Testing under Dependence via Semiparametric Graphical ModelsJie Liu, Chunming Zhang, Elizabeth S. Burnside, David Page. 955-963 [doi]
• Making Fisher Discriminant Analysis ScalableBojun Tu, Zhihua Zhang, Shusen Wang, Hui Qian. 964-972 [doi]
• Hierarchical Dirichlet Scaling ProcessDongwoo Kim, Alice H. Oh. 973-981 [doi]
• Approximation Analysis of Stochastic Gradient Langevin Dynamics by using Fokker-Planck Equation and Ito ProcessIssei Sato, Hiroshi Nakagawa. 982-990 [doi]
• A PAC-Bayesian bound for Lifelong LearningAnastasia Pentina, Christoph H. Lampert. 991-999 [doi]
• Communication-Efficient Distributed Optimization using an Approximate Newton-type MethodOhad Shamir, Nathan Srebro, Tong Zhang 0001. 1000-1008 [doi]
• Concept Drift Detection Through ResamplingMaayan Harel, Shie Mannor, Ran El-Yaniv, Koby Crammer. 1009-1017 [doi]
• Anti-differentiating approximation algorithms: A case study with min-cuts, spectral, and flowDavid Gleich, Michael Mahoney. 1018-1025 [doi]
• A Bayesian Wilcoxon signed-rank test based on the Dirichlet processAlessio Benavoli, Giorgio Corani, Francesca Mangili, Marco Zaffalon, Fabrizio Ruggeri. 1026-1034 [doi]
• Min-Max Problems on Factor GraphsSiamak (Moshen) Ravanbakhsh, Christopher Srinivasa, Brendan Frey, Russell Greiner. 1035-1043 [doi]
• Distributed Stochastic Gradient MCMCSungjin Ahn, Babak Shahbaba, Max Welling. 1044-1052 [doi]
• Nearest Neighbors Using Compact Sparse CodesAnoop Cherian. 1053-1061 [doi]
• Optimal Mean Robust Principal Component AnalysisFeiping Nie, Jianjun Yuan, Heng Huang. 1062-1070 [doi]
• Preference-Based Rank Elicitation using Statistical Models: The Case of MallowsRóbert Busa-Fekete, Eyke Hüllermeier, Balázs Szörényi. 1071-1079 [doi]
• Hierarchical Conditional Random Fields for Outlier Detection: An Application to Detecting Epileptogenic Cortical MalformationsBilal Ahmed, Thomas Thesen, Karen Blackmon, Yijun Zhao, Orrin Devinsky, Ruben Kuzniecky, Carla E. Brodley. 1080-1088 [doi]
• A Physics-Based Model Prior for Object-Oriented MDPsJonathan Scholz, Martin Levihn, Charles Lee Isbell Jr., David Wingate. 1089-1097 [doi]
• Outlier Path: A Homotopy Algorithm for Robust SVMShinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Ichiro Takeuchi. 1098-1106 [doi]
• Ensemble-Based Tracking: Aggregating Crowdsourced Structured Time Series DataNaiyan Wang, Dit-Yan Yeung. 1107-1115 [doi]
• Latent Confusion Analysis by Normalized Gamma ConstructionIssei Sato, Hisashi Kashima, Hiroshi Nakagawa. 1116-1124 [doi]
• Finito: A faster, permutable incremental gradient method for big data problemsAaron Defazio, Justin Domke, Tibério S. Caetano. 1125-1133 [doi]
• Ensemble Methods for Structured PredictionCorinna Cortes, Vitaly Kuznetsov, Mehryar Mohri. 1134-1142 [doi]
• Standardized Mutual Information for Clustering Comparisons: One Step Further in Adjustment for ChanceSimone Romano, James Bailey, Xuan Vinh Nguyen, Karin Verspoor. 1143-1151 [doi]
• Preserving Modes and Messages via Diverse Particle SelectionJason Pacheco, Silvia Zuffi, Michael J. Black, Erik B. Sudderth. 1152-1160 [doi]
• Nonlinear Information-Theoretic Compressive Measurement DesignLiming Wang, Abolfazl Razi, Miguel R. D. Rodrigues, A. Robert Calderbank, Lawrence Carin. 1161-1169 [doi]
• Dual Query: Practical Private Query Release for High Dimensional DataMarco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu. 1170-1178 [doi]
• Deep BoostingCorinna Cortes, Mehryar Mohri, Umar Syed. 1179-1187 [doi]
• Distributed Representations of Sentences and DocumentsQuoc V. Le, Tomas Mikolov. 1188-1196 [doi]
• Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov ModelsRobert McGibbon, Bharath Ramsundar, Mohammad Sultan, Gert Kiss, Vijay Pande. 1197-1205 [doi]
• Online Multi-Task Learning for Policy Gradient MethodsHaitham Bou-Ammar, Eric Eaton, Paul Ruvolo, Matthew E. Taylor. 1206-1214 [doi]
• Affinity Weighted EmbeddingJason Weston, Ron J. Weiss, Hector Yee. 1215-1223 [doi]
• Learning the Parameters of Determinantal Point Process KernelsRaja Hafiz Affandi, Emily B. Fox, Ryan P. Adams, Benjamin Taskar. 1224-1232 [doi]
• Discrete Chebyshev ClassifiersElad Eban, Elad Mezuman, Amir Globerson. 1233-1241 [doi]
• Deep AutoRegressive NetworksKarol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra. 1242-1250 [doi]
• A Convergence Rate Analysis for LogitBoost, MART and Their VariantPeng Sun, Tong Zhang, Jie Zhou. 1251-1259 [doi]
• Inferning with High Girth Graphical ModelsUri Heinemann, Amir Globerson. 1260-1268 [doi]
• Learning Latent Variable Gaussian Graphical ModelsZhaoshi Meng, Brian Eriksson, Alfred O. Hero III. 1269-1277 [doi]
• Stochastic Backpropagation and Approximate Inference in Deep Generative ModelsDanilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra. 1278-1286 [doi]
• One Practical Algorithm for Both Stochastic and Adversarial BanditsYevgeny Seldin, Aleksandrs Slivkins. 1287-1295 [doi]
• Robust and Efficient Kernel Hyperparameter Paths with GuaranteesJoachim Giesen, Sören Laue, Patrick Wieschollek. 1296-1304 [doi]
• Active Transfer Learning under Model ShiftXuezhi Wang, Tzu-Kuo Huang, Jeff Schneider. 1305-1313 [doi]
• Approximate Policy Iteration Schemes: A ComparisonBruno Scherrer. 1314-1322 [doi]
• Stable and Efficient Representation Learning with Nonnegativity ConstraintsTsung-Han Lin, H. T. Kung. 1323-1331 [doi]
• Sample Efficient Reinforcement Learning with Gaussian ProcessesRobert C. Grande, Thomas J. Walsh, Jonathan P. How. 1332-1340 [doi]
• Memory and Computation Efficient PCA via Very Sparse Random ProjectionsFarhad Pourkamali Anaraki, Shannon M. Hughes. 1341-1349 [doi]
• Time-Regularized Interrupting Options (TRIO)Timothy Mann, Daniel Mankowitz, Shie Mannor. 1350-1358 [doi]
• Randomized Nonlinear Component AnalysisDavid Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf. 1359-1367 [doi]
• High Order Regularization for Semi-Supervised Learning of Structured Output ProblemsYujia Li, Richard S. Zemel. 1368-1376 [doi]
• Transductive Learning with Multi-class Volume ApproximationGang Niu, Bo Dai, Marthinus Christoffel du Plessis, Masashi Sugiyama. 1377-1385 [doi]
• Methods of Moments for Learning Stochastic Languages: Unified Presentation and Empirical ComparisonBorja Balle, William L. Hamilton, Joelle Pineau. 1386-1394 [doi]
• Effective Bayesian Modeling of Groups of Related Count Time SeriesNicolas Chapados. 1395-1403 [doi]
• Variational Inference for Sequential Distance Dependent Chinese Restaurant ProcessSergey Bartunov, Dmitry Vetrov. 1404-1412 [doi]
• Discovering Latent Network Structure in Point Process DataScott W. Linderman, Ryan P. Adams. 1413-1421 [doi]
• A Kernel Independence Test for Random ProcessesKacper Chwialkowski, Arthur Gretton. 1422-1430 [doi]
• Learning to Disentangle Factors of Variation with Manifold InteractionScott Reed, Kihyuk Sohn, Yuting Zhang, Honglak Lee. 1431-1439 [doi]
• Learning Modular Structures from Network Data and Node VariablesElham Azizi, Edoardo Airoldi, James E. Galagan. 1440-1448 [doi]
• Probabilistic Partial Canonical Correlation AnalysisYusuke Mukuta, Tatsuya Harada. 1449-1457 [doi]
• Skip Context Tree SwitchingMarc G. Bellemare, Joel Veness, Erik Talvitie. 1458-1466 [doi]
• Lower Bounds for the Gibbs Sampler over Mixtures of GaussiansChristopher Tosh, Sanjoy Dasgupta. 1467-1475 [doi]
• Marginalized Denoising Auto-encoders for Nonlinear RepresentationsMinmin Chen, Kilian Q. Weinberger, Fei Sha, Yoshua Bengio. 1476-1484 [doi]
• Gaussian Processes for Bayesian Estimation in Ordinary Differential EquationsDavid Barber, Yali Wang. 1485-1493 [doi]
• Fast Multi-stage Submodular MaximizationKai Wei, Rishabh Iyer, Jeff A. Bilmes. 1494-1502 [doi]
• Programming by FeedbackMarc Schoenauer, Riad Akrour, Michèle Sebag, Jean-Christophe Souplet. 1503-1511 [doi]
• Probabilistic Matrix Factorization with Non-random Missing DataJosé Miguel Hernández-Lobato, Neil Houlsby, Zoubin Ghahramani. 1512-1520 [doi]
• Pursuit-Evasion Without Regret, with an Application to TradingLili Dworkin, Michael Kearns, Yuriy Nevmyvaka. 1521-1529 [doi]
• The f-Adjusted Graph Laplacian: a Diagonal Modification with a Geometric InterpretationSven Kurras, Ulrike von Luxburg, Gilles Blanchard. 1530-1538 [doi]
• Riemannian Pursuit for Big Matrix RecoveryMingkui Tan, Ivor W. Tsang, Li Wang, Bart Vandereycken, Sinno Jialin Pan. 1539-1547 [doi]
• Dynamic Programming Boosting for Discriminative Macro-Action DiscoveryLeonidas Lefakis, François Fleuret. 1548-1556 [doi]
• Online Stochastic Optimization under Correlated Bandit FeedbackMohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill. 1557-1565 [doi]
• Weighted Graph Clustering with Non-Uniform UncertaintiesYudong Chen, Shiau Hong Lim, Huan Xu. 1566-1574 [doi]
• GeNGA: A Generalization of Natural Gradient Ascent with Positive and Negative Convergence ResultsPhilip Thomas. 1575-1583 [doi]
• A Bayesian Framework for Online Classifier EnsembleQinxun Bai, Henry Lam, Stan Sclaroff. 1584-1592 [doi]
• Adaptivity and Optimism: An Improved Exponentiated Gradient AlgorithmJacob Steinhardt, Percy Liang. 1593-1601 [doi]
• Gaussian Approximation of Collective Graphical ModelsLi-Ping Liu, Daniel Sheldon, Thomas G. Dietterich. 1602-1610 [doi]
• On learning to localize objects with minimal supervisionHyun Oh Song, Ross B. Girshick, Stefanie Jegelka, Julien Mairal, Zaïd Harchaoui, Trevor Darrell. 1611-1619 [doi]
• Multiresolution Matrix FactorizationRisi Kondor, Nedelina Teneva, Vikas Garg. 1620-1628 [doi]
• Learnability of the Superset Label Learning ProblemLi-Ping Liu, Thomas G. Dietterich. 1629-1637 [doi]
• Taming the Monster: A Fast and Simple Algorithm for Contextual BanditsAlekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire. 1638-1646 [doi]
• Structured Recurrent Temporal Restricted Boltzmann MachinesRoni Mittelman, Benjamin Kuipers, Silvio Savarese, Honglak Lee. 1647-1655 [doi]
• Scalable and Robust Bayesian Inference via the Median PosteriorStanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson. 1656-1664 [doi]
• Kernel Adaptive Metropolis-HastingsDino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton. 1665-1673 [doi]
• Input Warping for Bayesian Optimization of Non-Stationary FunctionsJasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams. 1674-1682 [doi]
• Stochastic Gradient Hamiltonian Monte CarloTianQi Chen, Emily B. Fox, Carlos Guestrin. 1683-1691 [doi]
• A Deep Semi-NMF Model for Learning Hidden RepresentationsGeorge Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou, Björn Schuller. 1692-1700 [doi]
• Asynchronous Distributed ADMM for Consensus OptimizationRuiliang Zhang, James T. Kwok. 1701-1709 [doi]
• Spectral Regularization for Max-Margin Sequence TaggingAriadna Quattoni, Borja Balle, Xavier Carreras, Amir Globerson. 1710-1718 [doi]
• Learning by Stretching Deep NetworksGaurav Pandey, Ambedkar Dukkipati. 1719-1727 [doi]
• Nonnegative Sparse PCA with Provable GuaranteesMegasthenis Asteris, Dimitris S. Papailiopoulos, Alexandros G. Dimakis. 1728-1736 [doi]
• Active Learning of Parameterized SkillsBruno Castro da Silva, George Konidaris, Andrew G. Barto. 1737-1745 [doi]
• Learning Ordered Representations with Nested DropoutOren Rippel, Michael A. Gelbart, Ryan P. Adams. 1746-1754 [doi]
• Learning the Irreducible Representations of Commutative Lie GroupsTaco Cohen, Max Welling. 1755-1763 [doi]
• Towards End-To-End Speech Recognition with Recurrent Neural NetworksAlex Graves, Navdeep Jaitly. 1764-1772 [doi]
• Multi-period Trading Prediction Markets with Connections to Machine LearningJinli Hu, Amos J. Storkey. 1773-1781 [doi]
• Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural NetsDiederik P. Kingma, Max Welling. 1782-1790 [doi]
• Neural Variational Inference and Learning in Belief NetworksAndriy Mnih, Karol Gregor. 1791-1799 [doi]
• Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway TensorsPiyush Rai, Yingjian Wang, Shengbo Guo, Gary Chen, David B. Dunson, Lawrence Carin. 1800-1808 [doi]
• Beta Diffusion TreesCreighton Heaukulani, David A. Knowles, Zoubin Ghahramani. 1809-1817 [doi]
• Learning Character-level Representations for Part-of-Speech TaggingCícero Nogueira dos Santos, Bianca Zadrozny. 1818-1826 [doi]
• Saddle Points and Accelerated Perceptron AlgorithmsAdams Wei Yu, Fatma Kilinç-Karzan, Jaime G. Carbonell. 1827-1835 [doi]
• Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and MaximizationHua Wang, Feiping Nie, Heng Huang. 1836-1844 [doi]
• Learning from Contagion (Without Timestamps)Kareem Amin, Hoda Heidari, Michael Kearns. 1845-1853 [doi]
• Stochastic Variational Inference for Bayesian Time Series ModelsMatthew Johnson, Alan S. Willsky. 1854-1862 [doi]
• A Clockwork RNNJan Koutník, Klaus Greff, Faustino J. Gomez, Jürgen Schmidhuber. 1863-1871 [doi]
• Estimating Latent-Variable Graphical Models using Moments and LikelihoodsArun Tejasvi Chaganty, Percy Liang. 1872-1880 [doi]
• Universal Matrix CompletionSrinadh Bhojanapalli, Prateek Jain 0002. 1881-1889 [doi]
• Finding Dense Subgraphs via Low-Rank Bilinear OptimizationDimitris S. Papailiopoulos, Ioannis Mitliagkas, Alexandros G. Dimakis, Constantine Caramanis. 1890-1898 [doi]
• Compositional Morphology for Word Representations and Language ModellingJan A. Botha, Phil Blunsom. 1899-1907 [doi]
• Learning Polynomials with Neural NetworksAlexandr Andoni, Rina Panigrahy, Gregory Valiant, Li Zhang 0001. 1908-1916 [doi]
• Exponential Family Matrix Completion under Structural ConstraintsSuriya Gunasekar, Pradeep Ravikumar, Joydeep Ghosh. 1917-1925 [doi]
• Sample-based approximate regularizationPhilip Bachman, Amir Massoud Farahmand, Doina Precup. 1926-1934 [doi]
• A Compilation Target for Probabilistic Programming LanguagesBrooks Paige, Frank Wood. 1935-1943 [doi]
• Adaptive Monte Carlo via Bandit AllocationJames Neufeld, András György, Csaba Szepesvári, Dale Schuurmans. 1944-1952 [doi]
• Efficient Dimensionality Reduction for High-Dimensional Network EstimationSafiye Celik, Benjamin A. Logsdon, Su-In Lee. 1953-1961 [doi]
• Deterministic Anytime Inference for Stochastic Continuous-Time Markov ProcessesE. Busra Celikkaya, Christian R. Shelton. 1962-1970 [doi]
• Doubly Stochastic Variational Bayes for non-Conjugate InferenceMichalis K. Titsias, Miguel Lázaro-Gredilla. 1971-1979 [doi]
• Efficient Learning of Mahalanobis Metrics for RankingDaryl Lim, Gert R. G. Lanckriet. 1980-1988 [doi]
• GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is RareArpit Agarwal, Harikrishna Narasimhan, Shivaram Kalyanakrishnan, Shivani Agarwal 0001. 1989-1997 [doi]
• A reversible infinite HMM using normalised random measuresDavid A. Knowles, Zoubin Ghahramani, Konstantina Palla. 1998-2006 [doi]
• Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image ProcessingBenjamin Haeffele, Eric Young, René Vidal. 2007-2015 [doi]
• Influence Function Learning in Information Diffusion NetworksNan Du, Yingyu Liang, Maria-Florina Balcan, Le Song. 2016-2024 [doi]