Table of Contents

• Minimax Gaussian Classification & ClusteringTianyang Li, Xinyang Yi, Constantine Caramanis, Pradeep Ravikumar. 1-9 [doi]
• Conditions beyond treewidth for tightness of higher-order LP relaxationsMark Rowland, Aldo Pacchiano, Adrian Weller. 10-18 [doi]
• Large-Scale Data-Dependent Kernel ApproximationCatalin Ionescu, Alin-Ionut Popa, Cristian Sminchisescu. 19-27 [doi]
• Clustering from Multiple Uncertain ExpertsYale Chang, Junxiang Chen, Michael H. Cho, Peter J. Castaldi, Edwin K. Silverman, Jennifer G. Dy. 28-36 [doi]
• Online Nonnegative Matrix Factorization with General DivergencesRenbo Zhao, Vincent Yan Fu Tan, Huan Xu. 37-45 [doi]
• ASAGA: Asynchronous Parallel SAGARémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien. 46-54 [doi]
• Lower Bounds on Active Learning for Graphical Model SelectionJonathan Scarlett, Volkan Cevher. 55-64 [doi]
• Non-square matrix sensing without spurious local minima via the Burer-Monteiro approachDohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi. 65-74 [doi]
• Sparse Accelerated Exponential WeightsPierre Gaillard, Olivier Wintenberger. 75-82 [doi]
• On the Learnability of Fully-Connected Neural NetworksYuchen Zhang, Jason D. Lee, Martin J. Wainwright, Michael I. Jordan. 83-91 [doi]
• An Information-Theoretic Route from Generalization in Expectation to Generalization in ProbabilityIbrahim M. Alabdulmohsin. 92-100 [doi]
• Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm SelectionLijie Chen, Jian Li, Mingda Qiao. 101-110 [doi]
• Guaranteed Non-convex Optimization: Submodular Maximization over Continuous DomainsAndrew An Bian, Baharan Mirzasoleiman, Joachim M. Buhmann, Andreas Krause 0001. 111-120 [doi]
• Tensor-Dictionary Learning with Deep Kruskal-Factor AnalysisAndrew Stevens, Yunchen Pu, Yannan Sun, Gregory Spell, Lawrence Carin. 121-129 [doi]
• Consistent and Efficient Nonparametric Different-Feature SelectionSatoshi Hara, Takayuki Katsuki, Hiroki Yanagisawa, Takafumi Ono, Ryo Okamoto, Shigeki Takeuchi. 130-138 [doi]
• Annular Augmentation SamplingFrancois Fagan, Jalaj Bhandari, John Cunningham. 139-147 [doi]
• Less than a Single Pass: Stochastically Controlled Stochastic GradientLihua Lei, Michael I. Jordan. 148-156 [doi]
• Learning Time Series Detection Models from Temporally Imprecise LabelsRoy Adams, Ben Marlin. 157-165 [doi]
• Learning Cost-Effective and Interpretable Treatment RegimesHimabindu Lakkaraju, Cynthia Rudin. 166-175 [doi]
• Linear Thompson Sampling RevisitedMarc Abeille, Alessandro Lazaric. 176-184 [doi]
• A Sub-Quadratic Exact Medoid AlgorithmJames Newling, François Fleuret. 185-193 [doi]
• Minimax Density Estimation for Growing DimensionDaniel McDonald. 194-203 [doi]
• Estimating Density Ridges by Direct Estimation of Density-Derivative-RatiosHiroaki Sasaki, Takafumi Kanamori, Masashi Sugiyama. 204-212 [doi]
• Learning Theory for Conditional Risk MinimizationAlexander Zimin, Christoph H. Lampert. 213-222 [doi]
• Near-optimal Bayesian Active Learning with Correlated and Noisy TestsYuxin Chen, Seyed Hamed Hassani, Andreas Krause 0001. 223-231 [doi]
• Learning Nash Equilibrium for General-Sum Markov Games from Batch DataJulien Pérolat, Florian Strub, Bilal Piot, Olivier Pietquin. 232-241 [doi]
• Distance Covariance AnalysisBenjamin Cowley 0002, João D. Semedo, Amin Zandvakili, Matthew A. Smith, Adam Kohn, Byron M. Yu. 242-251 [doi]
• Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex RelaxationSohail Bahmani, Justin Romberg. 252-260 [doi]
• Regret Bounds for Lifelong LearningPierre Alquier, The Tien Mai, Massimiliano Pontil. 261-269 [doi]
• Poisson intensity estimation with reproducing kernelsSeth Flaxman, Yee Whye Teh, Dino Sejdinovic. 270-279 [doi]
• Generalized Pseudolikelihood Methods for Inverse Covariance EstimationAlnur Ali, Kshitij Khare, Sang-Yun Oh, Bala Rajaratnam. 280-288 [doi]
• Removing Phase Transitions from Gibbs MeasuresIan Fellows, Mark Handcock. 289-297 [doi]
• Performance Bounds for Graphical Record LinkageRebecca C. Steorts, Matt Barnes, Willie Neiswanger. 298-306 [doi]
• Regret Bounds for Transfer Learning in Bayesian OptimisationAlistair Shilton, Sunil Kumar Gupta 0001, Santu Rana, Svetha Venkatesh. 307-315 [doi]
• Scaling Submodular Maximization via Pruned Submodularity GraphsTianyi Zhou, Hua Ouyang, Jeff Blimes, Yi Chang, Carlos Guestrin. 316-324 [doi]
• Localized Lasso for High-Dimensional RegressionMakoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski. 325-333 [doi]
• Encrypted Accelerated Least Squares RegressionPedro M. Esperança, Louis J. M. Aslett, Chris C. Holmes. 334-343 [doi]
• Random Consensus Robust PCADaniel L. Pimentel-Alarcon, Robert D. Nowak. 344-352 [doi]
• Gray-box Inference for Structured Gaussian Process ModelsPietro Galliani, Amir Dezfouli, Edwin V. Bonilla, Novi Quadrianto. 353-361 [doi]
• Frank-Wolfe Algorithms for Saddle Point ProblemsGauthier Gidel, Tony Jebara, Simon Lacoste-Julien. 362-371 [doi]
• A Framework for Optimal Matching for Causal InferenceNathan Kallus. 372-381 [doi]
• Quantifying the accuracy of approximate diffusions and Markov chainsJonathan Huggins, James Zou. 382-391 [doi]
• Stochastic Rank-1 BanditsSumeet Katariya, Branislav Kveton, Csaba Szepesvári, Claire Vernade, Zheng Wen. 392-401 [doi]
• On the Troll-Trust Model for Edge Sign Prediction in Social NetworksGéraud Le Falher, Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale. 402-411 [doi]
• Online Optimization of Smoothed Piecewise Constant FunctionsVincent Cohen-Addad, Varun Kanade. 412-420 [doi]
• Combinatorial Topic Models using Small-Variance AsymptoticsKe Jiang, Suvrit Sra, Brian Kulis. 421-429 [doi]
• ConvNets with Smooth Adaptive Activation Functions for RegressionLe Hou, Dimitris Samaras, Tahsin M. Kurç, Yi Gao, Joel H. Saltz. 430-439 [doi]
• Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive ModelsSejun Park, Yunhun Jang, Andreas Galanis, Jinwoo Shin, Daniel Stefankovic, Eric Vigoda. 440-449 [doi]
• Efficient Rank Aggregation via Lehmer CodesPan Li, Arya Mazumdar, Olgica Milenkovic. 450-459 [doi]
• Nonlinear ICA of Temporally Dependent Stationary SourcesAapo Hyvärinen, Hiroshi Morioka. 460-469 [doi]
• Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann MachinesAtsushi Nitanda, Taiji Suzuki. 470-478 [doi]
• Global Convergence of Non-Convex Gradient Descent for Computing Matrix SquarerootPrateek Jain 0002, Chi Jin, Sham M. Kakade, Praneeth Netrapalli. 479-488 [doi]
• Reparameterization Gradients through Acceptance-Rejection Sampling AlgorithmsChristian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman, David M. Blei. 489-498 [doi]
• Asymptotically exact inference in differentiable generative modelsMatthew Graham, Amos Storkey. 499-508 [doi]
• Decentralized Collaborative Learning of Personalized Models over NetworksPaul Vanhaesebrouck, Aurélien Bellet, Marc Tommasi. 509-517 [doi]
• Contextual Bandits with Latent Confounders: An NMF ApproachRajat Sen, Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sanjay Shakkottai. 518-527 [doi]
• Fast Bayesian Optimization of Machine Learning Hyperparameters on Large DatasetsAaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter. 528-536 [doi]
• Least-Squares Log-Density Gradient Clustering for Riemannian ManifoldsMina Ashizawa, Hiroaki Sasaki, Tomoya Sakai, Masashi Sugiyama. 537-546 [doi]
• Fast column generation for atomic norm regularizationMarina Vinyes, Guillaume Obozinski. 547-556 [doi]
• Bayesian Hybrid Matrix Factorisation for Data IntegrationThomas Brouwer, Pietro Liò. 557-566 [doi]
• Co-Occurring Directions Sketching for Approximate Matrix MultiplyYoussef Mroueh, Etienne Marcheret, Vaibhava Goel. 567-575 [doi]
• Exploration-Exploitation in MDPs with OptionsRonan Fruit, Alessandro Lazaric. 576-584 [doi]
• Local Perturb-and-MAP for Structured PredictionGedas Bertasius, Qiang Liu, Lorenzo Torresani, Jianbo Shi. 585-594 [doi]
• Gradient Boosting on Stochastic Data StreamsHanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell. 595-603 [doi]
• Online Learning and Blackwell Approachability with Partial Monitoring: Optimal Convergence RatesJoon Kwon, Vianney Perchet. 604-613 [doi]
• Tensor Decompositions via Two-Mode Higher-Order SVD (HOSVD)Miaoyan Wang, Yun S. Song. 614-622 [doi]
• Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiersMeelis Kull, Telmo de Menezes e Silva Filho, Peter A. Flach. 623-631 [doi]
• Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric BayesFeras Saad, Vikash Mansinghka. 632-641 [doi]
• High-dimensional Time Series Clustering via Cross-PredictabilityDezhi Hong, Quanquan Gu, Kamin Whitehouse. 642-651 [doi]
• Minimax Approach to Variable Fidelity Data InterpolationAlexey Zaytsev, Evgeny Burnaev. 652-661 [doi]
• Data Driven Resource Allocation for Distributed LearningTravis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan, Alexander J. Smola. 662-671 [doi]
• Learning Nonparametric Forest Graphical Models with Prior InformationYuancheng Zhu, Zhe Liu, Siqi Sun. 672-680 [doi]
• Sparse Randomized Partition Trees for Nearest Neighbor SearchKaushik Sinha, Omid Keivani. 681-689 [doi]
• Horde of Bandits using Gaussian Markov Random FieldsSharan Vaswani, Mark Schmidt, Laks V. S. Lakshmanan. 690-699 [doi]
• Random projection design for scalable implicit smoothing of randomly observed stochastic processesFrancois Belletti, Evan R. Sparks, Alexandre M. Bayen, Joseph Gonzalez. 700-708 [doi]
• Trading off Rewards and Errors in Multi-Armed BanditsAkram Erraqabi, Alessandro Lazaric, Michal Valko, Emma Brunskill, Yun-En Liu. 709-717 [doi]
• Adaptive ADMM with Spectral Penalty Parameter SelectionZheng Xu 0002, Mário A. T. Figueiredo, Tom Goldstein. 718-727 [doi]
• The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear BanditsTor Lattimore, Csaba Szepesvári. 728-737 [doi]
• Dynamic Collaborative Filtering With Compound Poisson FactorizationGhassen Jerfel, Mehmet Emin Basbug, Barbara E. Engelhardt. 738-747 [doi]
• Rank Aggregation and Prediction with Item FeaturesKai-yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon. 748-756 [doi]
• Robust and Efficient Computation of Eigenvectors in a Generalized Spectral Method for Constrained ClusteringChengming Jiang, Huiqing Xie, Zhaojun Bai. 757-766 [doi]
• Information-theoretic limits of Bayesian network structure learningAsish Ghoshal, Jean Honorio. 767-775 [doi]
• Markov Chain Truncation for Doubly-Intractable InferenceColin Wei, Iain Murray. 776-784 [doi]
• Regression Uncertainty on the GrassmannianYi Hong, Xiao Yang, Roland Kwitt, Martin Styner, Marc Niethammer. 785-793 [doi]
• Attributing HacksZiqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng. 794-802 [doi]
• Unsupervised Sequential Sensor AcquisitionManjesh Kumar Hanawal, Csaba Szepesvári, Venkatesh Saligrama. 803-811 [doi]
• A Stochastic Nonconvex Splitting Method for Symmetric Nonnegative Matrix FactorizationSongtao Lu, Mingyi Hong, Zhengdao Wang. 812-821 [doi]
• Hierarchically-partitioned Gaussian Process ApproximationByung Jun Lee, Jongmin Lee, Kee-Eung Kim. 822-831 [doi]
• Scalable Learning of Non-Decomposable ObjectivesElad Eban, Mariano Schain, Alan Mackey, Ariel Gordon, Ryan Rifkin, Gal Elidan. 832-840 [doi]
• CPSG-MCMC: Clustering-Based Preprocessing method for Stochastic Gradient MCMCTianfan Fu, Zhihua Zhang. 841-850 [doi]
• Comparison-Based Nearest Neighbor SearchSiavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg. 851-859 [doi]
• A Unified Optimization View on Generalized Matching Pursuit and Frank-WolfeFrancesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi. 860-868 [doi]
• Faster Coordinate Descent via Adaptive Importance SamplingDmytro Perekrestenko, Volkan Cevher, Martin Jaggi. 869-877 [doi]
• Conjugate-Computation Variational Inference: Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate ModelsMohammad Emtiyaz Khan, Wu Lin. 878-887 [doi]
• Hit-and-Run for Sampling and Planning in Non-Convex SpacesYasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek. 888-895 [doi]
• DP-EM: Differentially Private Expectation MaximizationMijung Park, James R. Foulds, Kamalika Choudhary, Max Welling. 896-904 [doi]
• On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe PriorJuho Piironen, Aki Vehtari. 905-913 [doi]
• Bayesian Learning and Inference in Recurrent Switching Linear Dynamical SystemsScott W. Linderman, Matthew Johnson, Andrew Miller, Ryan Adams, David M. Blei, Liam Paninski. 914-922 [doi]
• Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient DescentPan Xu, Tingting Zhang, Quanquan Gu. 923-932 [doi]
• Minimax-optimal semi-supervised regression on unknown manifoldsAmit Moscovich, Ariel Jaffe, Boaz Nadler. 933-942 [doi]
• Improved Strongly Adaptive Online Learning using Coin BettingKwang-Sung Jun, Francesco Orabona, Stephen Wright, Rebecca Willett. 943-951 [doi]
• Black-box Importance SamplingQiang Liu, Jason D. Lee. 952-961 [doi]
• Fairness Constraints: Mechanisms for Fair ClassificationMuhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rogriguez, Krishna P. Gummadi. 962-970 [doi]
• Frequency Domain Predictive Modelling with Aggregated DataAvradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo. 971-980 [doi]
• A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix EstimationLingxiao Wang, Xiao Zhang, Quanquan Gu. 981-990 [doi]
• A New Class of Private Chi-Square Hypothesis TestsRyan Rogers, Daniel Kifer. 991-1000 [doi]
• A Learning Theory of Ranking AggregationAnna Korba, Stéphan Clémençon, Eric Sibony. 1001-1010 [doi]
• Anomaly Detection in Extreme Regions via Empirical MV-sets on the SphereAlbert Thomas, Stéphan Clémençon, Alexandre Gramfort, Anne Sabourin. 1011-1019 [doi]
• Structured adaptive and random spinners for fast machine learning computationsMariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Francois Fagan, Cédric Gouy-Pailler, Anne Morvan, Nouri Sakr, Tamás Sarlós, Jamal Atif. 1020-1029 [doi]
• Complementary Sum Sampling for Likelihood Approximation in Large Scale ClassificationAleksandar Botev, Bowen Zheng, David Barber. 1030-1038 [doi]
• Learning Optimal InterventionsJonas Mueller, David Reshef, George Du, Tommi S. Jaakkola. 1039-1047 [doi]
• A Lower Bound on the Partition Function of Attractive Graphical Models in the Continuous CaseNicholas Ruozzi. 1048-1056 [doi]
• Scalable Variational Inference for Super Resolution MicroscopyRuoxi Sun, Evan Archer, Liam Paninski. 1057-1065 [doi]
• Linear Convergence of Stochastic Frank Wolfe VariantsDonald Goldfarb, Garud Iyengar, Chaoxu Zhou. 1066-1074 [doi]
• Sequential Graph Matching with Sequential Monte CarloSeong-Hwan Jun, Samuel W. K. Wong, James V. Zidek, Alexandre Bouchard-Côté. 1075-1084 [doi]
• Fast rates with high probability in exp-concave statistical learningNishant Mehta. 1085-1093 [doi]
• Generalization Error of Invariant ClassifiersJure Sokolic, Raja Giryes, Guillermo Sapiro, Miguel R. D. Rodrigues. 1094-1103 [doi]
• Learning with Feature Feedback: from Theory to PracticeStefanos Poulis, Sanjoy Dasgupta. 1104-1113 [doi]
• Optimistic Planning for the Stochastic Knapsack ProblemCiara Pike-Burke, Steffen Grünewälder. 1114-1122 [doi]
• 1-normsRaman Sankaran, Francis R. Bach, Chiranjib Bhattacharyya. 1123-1131 [doi]
• Tracking Objects with Higher Order Interactions via Delayed Column GenerationShaofei Wang, Steffen Wolf, Charless C. Fowlkes, Julian Yarkony. 1132-1140 [doi]
• Belief Propagation in Conditional RBMs for Structured PredictionWei Ping, Alexander T. Ihler. 1141-1149 [doi]
• Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional DataJialei Wang, Jason D. Lee, Mehrdad Mahdavi, Mladen Kolar, Nati Srebro. 1150-1158 [doi]
• Finite-sum Composition Optimization via Variance Reduced Gradient DescentXiangru Lian, Mengdi Wang, Ji Liu. 1159-1167 [doi]
• A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical ModelsBeilun Wang, Ji Gao, Yanjun Qi. 1168-1177 [doi]
• Communication-efficient Distributed Sparse Linear Discriminant AnalysisLu Tian, Quanquan Gu. 1178-1187 [doi]
• Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal StorageAlp Yurtsever, Madeleine Udell, Joel A. Tropp, Volkan Cevher. 1188-1196 [doi]
• Modal-set estimation with an application to clusteringHeinrich Jiang, Samory Kpotufe. 1197-1206 [doi]
• Compressed Least Squares Regression revisitedMartin Slawski. 1207-1215 [doi]
• Diverse Neural Network Learns True Target FunctionsBo Xie 0002, Yingyu Liang, Le Song. 1216-1224 [doi]
• Local Group Invariant Representations via Orbit EmbeddingsAnant Raj, Abhishek Kumar 0001, Youssef Mroueh, Tom Fletcher, Bernhard Schölkopf. 1225-1235 [doi]
• Relativistic Monte CarloXiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian Vollmer. 1236-1245 [doi]
• Thompson Sampling for Linear-Quadratic Control ProblemsMarc Abeille, Alessandro Lazaric. 1246-1254 [doi]
• Fast Classification with Binary PrototypesKai Zhong, Ruiqi Guo, Sanjiv Kumar, Bowei Yan, David Simcha, Inderjit S. Dhillon. 1255-1263 [doi]
• Prediction Performance After Learning in Gaussian Process RegressionJohan Wågberg, Dave Zachariah, Thomas B. Schön, Petre Stoica. 1264-1272 [doi]
• Communication-Efficient Learning of Deep Networks from Decentralized DataBrendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas. 1273-1282 [doi]
• Learning Structured Weight Uncertainty in Bayesian Neural NetworksShengyang Sun, Changyou Chen, Lawrence Carin. 1283-1292 [doi]
• Signal-based Bayesian Seismic MonitoringDavid Moore, Stuart J. Russell. 1293-1301 [doi]
• Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random FieldsYoungsuk Park, David Hallac, Stephen P. Boyd, Jure Leskovec. 1302-1310 [doi]
• Discovering and Exploiting Additive Structure for Bayesian OptimizationJacob R. Gardner, Chuan Guo, Kilian Q. Weinberger, Roman Garnett, Roger Grosse. 1311-1319 [doi]
• Lipschitz Density-Ratios, Structured Data, and Data-driven TuningSamory Kpotufe. 1320-1328 [doi]
• Spatial Decompositions for Large Scale SVMsPhilipp Thomann, Ingrid Blaschzyk, Mona Meister, Ingo Steinwart. 1329-1337 [doi]
• Inference Compilation and Universal Probabilistic ProgrammingTuan Anh Le, Atilim Gunes Baydin, Frank Wood. 1338-1348 [doi]
• Active Positive Semidefinite Matrix Completion: Algorithms, Theory and ApplicationsAniruddha Bhargava, Ravi Ganti, Robert D. Nowak. 1349-1357 [doi]
• Information Projection and Approximate Inference for Structured Sparse VariablesRajiv Khanna, Joydeep Ghosh, Rusell Poldrack, Oluwasanmi Koyejo. 1358-1366 [doi]
• On the Interpretability of Conditional Probability Estimates in the Agnostic SettingYihan Gao, Aditya G. Parameswaran, Jian Peng. 1367-1374 [doi]
• Linking Micro Event History to Macro Prediction in Point Process ModelsYichen Wang, Xiaojing Ye, Hao-Min Zhou, Hongyuan Zha, Le Song. 1375-1384 [doi]
• Initialization and Coordinate Optimization for Multi-way MatchingDa Tang, Tony Jebara. 1385-1393 [doi]
• Optimal Recovery of Tensor SlicesVivek F. Farias, Andrew Li. 1394-1402 [doi]
• Efficient Online Multiclass Prediction on Graphs via Surrogate LossesAlexander Rakhlin, Karthik Sridharan. 1403-1411 [doi]
• Distribution of Gaussian Process Arc LengthsJustin Bewsher, Alessandra Tosi, Michael Osborne, Stephen Roberts. 1412-1420 [doi]
• Distributed Adaptive Sampling for Kernel Matrix ApproximationDaniele Calandriello, Alessandro Lazaric, Michal Valko. 1421-1429 [doi]
• Binary and Multi-Bit Coding for Stable Random ProjectionsPing Li 0001. 1430-1438 [doi]
• Spectral Methods for Correlated Topic ModelsForough Arabshahi, Anima Anandkumar. 1439-1447 [doi]
• Label Filters for Large Scale Multilabel ClassificationAlexandru Niculescu-Mizil, Ehsan Abbasnejad. 1448-1457 [doi]
• Learning from Conditional Distributions via Dual EmbeddingsBo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song. 1458-1467 [doi]
• Sequential Multiple Hypothesis Testing with Type I Error ControlAlan Malek, Sumeet Katariya, Yinlam Chow, Mohammad Ghavamzadeh. 1468-1476 [doi]
• A Maximum Matching Algorithm for Basis Selection in Spectral LearningAriadna Quattoni, Xavier Carreras, Matthias Gallé. 1477-1485 [doi]
• Value-Aware Loss Function for Model-based Reinforcement LearningAmir Massoud Farahmand, André Barreto, Daniel Nikovski. 1486-1494 [doi]
• Convergence Rate of Stochastic k-meansCheng Tang, Claire Monteleoni. 1495-1503 [doi]
• Automated Inference with Adaptive BatchesSoham De, Abhay Kumar Yadav, David W. Jacobs, Tom Goldstein. 1504-1513 [doi]
• Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual DecompositionJiong Zhang, Ian En-Hsu Yen, Pradeep Ravikumar, Inderjit S. Dhillon. 1514-1522 [doi]
• Robust Causal Estimation in the Large-Sample Limit without Strict FaithfulnessIoan Gabriel Bucur, Tom Claassen, Tom Heskes. 1523-1531 [doi]
• Learning Graphical Games from Behavioral Data: Sufficient and Necessary ConditionsAsish Ghoshal, Jean Honorio. 1532-1540 [doi]
• Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical ModelsAnkit Anand, Ritesh Noothigattu, Parag Singla, Mausam. 1541-1549 [doi]
• Greedy Direction Method of Multiplier for MAP Inference of Large Output DomainXiangru Huang, Ian En-Hsu Yen, Ruohan Zhang, Qixing Huang, Pradeep Ravikumar, Inderjit S. Dhillon. 1550-1559 [doi]
• Scalable Greedy Feature Selection via Weak SubmodularityRajiv Khanna, Ethan Elenberg, Alexandros G. Dimakis, Sahand Negahban, Joydeep Ghosh. 1560-1568 [doi]