Table of Contents

• Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family MixturesMario Lucic, Olivier Bachem, Andreas Krause 0001. 1-9 [doi]
• Revealing Graph Bandits for Maximizing Local InfluenceAlexandra Carpentier, Michal Valko. 10-18 [doi]
• Convex Block-sparse Linear Regression with Expanders - ProvablyAnastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad, Quoc Tran-Dinh, Luca Baldassarre, Volkan Cevher. 19-27 [doi]
• C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite CachingDaniel Ritchie, Andreas Stuhlmüller, Noah D. Goodman. 28-37 [doi]
• Clamping Improves TRW and Mean Field ApproximationsAdrian Weller, Justin Domke. 38-46 [doi]
• Tightness of LP Relaxations for Almost Balanced ModelsAdrian Weller, Mark Rowland, David Sontag. 47-55 [doi]
• Control Functionals for Quasi-Monte Carlo IntegrationChris J. Oates, Mark A. Girolami. 56-65 [doi]
• Probability Inequalities for Kernel Embeddings in Sampling without ReplacementMarkus Schneider. 66-74 [doi]
• Sparse Representation of Multivariate Extremes with Applications to Anomaly RankingNicolas Goix, Anne Sabourin, Stéphan Clémençon. 75-83 [doi]
• A Robust-Equitable Copula Dependence Measure for Feature SelectionYale Chang, Yi Li, A. Adam Ding, Jennifer Dy. 84-92 [doi]
• Random Forest for the Contextual Bandit ProblemRaphaël Féraud, Robin Allesiardo, Tanguy Urvoy, Fabrice Clérot. 93-101 [doi]
• Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and DynamicsMichael Herman, Tobias Gindele, Jörg Wagner, Felix Schmitt, Wolfram Burgard. 102-110 [doi]
• Learning Sparse Additive Models with Interactions in High DimensionsHemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause 0001. 111-120 [doi]
• Bipartite Correlation Clustering: Maximizing AgreementsMegasthenis Asteris, Anastasios Kyrillidis, Dimitris S. Papailiopoulos, Alexandros G. Dimakis. 121-129 [doi]
• Breaking Sticks and Ambiguities with Adaptive Skip-gramSergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry P. Vetrov. 130-138 [doi]
• Top Arm Identification in Multi-Armed Bandits with Batch Arm PullsKwang-Sung Jun, Kevin G. Jamieson, Robert D. Nowak, Xiaojin Zhu 0001. 139-148 [doi]
• Limits on Sparse Support Recovery via Linear Sketching with Random Expander MatricesJonathan Scarlett, Volkan Cevher. 149-158 [doi]
• Maximum Likelihood for Variance Estimation in High-Dimensional Linear ModelsLee H. Dicker, Murat A. Erdogdu. 159-167 [doi]
• Scalable Gaussian Process Classification via Expectation PropagationDaniel Hernández-Lobato, José Miguel Hernández-Lobato. 168-176 [doi]
• Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster RatesLingxiao Wang, Xiang Ren, Quanquan Gu. 177-185 [doi]
• On the Reducibility of Submodular FunctionsJincheng Mei, Hao Zhang, Bao-Liang Lu. 186-194 [doi]
• Accelerated Stochastic Gradient Descent for Minimizing Finite SumsAtsushi Nitanda. 195-203 [doi]
• Fast Convergence of Online Pairwise Learning AlgorithmsMartin Boissier 0002, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou. 204-212 [doi]
• Computationally Efficient Bayesian Learning of Gaussian Process State Space ModelsAndreas Svensson, Arno Solin, Simo Särkkä, Thomas B. Schön. 213-221 [doi]
• Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden VariablesYaniv Tenzer, Gal Elidan. 222-230 [doi]
• On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic ProcessesAlexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani. 231-239 [doi]
• Non-stochastic Best Arm Identification and Hyperparameter OptimizationKevin G. Jamieson, Ameet Talwalkar. 240-248 [doi]
• A Linearly-Convergent Stochastic L-BFGS AlgorithmPhilipp Moritz, Robert Nishihara, Michael I. Jordan. 249-258 [doi]
• No Regret Bound for Extreme BanditsRobert Nishihara, David Lopez-Paz, Léon Bottou. 259-267 [doi]
• Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse PerturbationsAnima Anandkumar, Prateek Jain 0002, Yang Shi, U. N. Niranjan. 268-276 [doi]
• Online Learning to Rank with Feedback at the TopSougata Chaudhuri, Ambuj Tewari. 277-285 [doi]
• Survey Propagation beyond Constraint Satisfaction ProblemsChristopher Srinivasa, Siamak Ravanbakhsh, Brendan J. Frey. 286-295 [doi]
• Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) ModelsBalázs Csanád Csáji. 296-304 [doi]
• CRAFT: ClusteR-specific Assorted Feature selecTionVikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola. 305-313 [doi]
• Time-Varying Gaussian Process Bandit OptimizationIlija Bogunovic, Jonathan Scarlett, Volkan Cevher. 314-323 [doi]
• Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index PoliciesWeici Hu, Peter I. Frazier. 324-332 [doi]
• Bayesian Markov Blanket EstimationDinu Kaufmann, Sonali Parbhoo, Aleksander Wieczorek, Sebastian Keller, David Adametz, Volker Roth 0001. 333-341 [doi]
• Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data AugmentationSøren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen, John W. Fisher III, Lars Kai Hansen. 342-350 [doi]
• Unsupervised Ensemble Learning with Dependent ClassifiersAriel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger. 351-360 [doi]
• Multi-Level Cause-Effect SystemsKrzysztof Chalupka, Frederick Eberhardt, Pietro Perona. 361-369 [doi]
• Deep Kernel LearningAndrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing. 370-378 [doi]
• Nearly Optimal Classification for SemimetricsLee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch. 379-388 [doi]
• Latent Point Process AllocationChris M. Lloyd, Tom Gunter, Michael A. Osborne, Stephen J. Roberts, Tom Nickson. 389-397 [doi]
• K2-ABC: Approximate Bayesian Computation with Kernel EmbeddingsMijung Park, Wittawat Jitkrittum, Dino Sejdinovic. 398-407 [doi]
• Bayesian Generalised Ensemble Markov Chain Monte CarloJes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg. 408-416 [doi]
• A Lasso-based Sparse Knowledge Gradient Policy for Sequential Optimal LearningYan Li 0013, Han Liu, Warren B. Powell. 417-425 [doi]
• Optimal Statistical and Computational Rates for One Bit Matrix CompletionRenkun Ni, Quanquan Gu. 426-434 [doi]
• PAC-Bayesian Bounds based on the Rényi DivergenceLuc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy. 435-444 [doi]
• Simple and Scalable Constrained Clustering: a Generalized Spectral MethodMihai Cucuringu, Ioannis Koutis, Sanjay Chawla, Gary L. Miller, Richard Peng. 445-454 [doi]
• Geometry Aware Mappings for High Dimensional Sparse FactorsAvradeep Bhowmik, Nathan Liu, ErHeng Zhong, Badri Narayan Bhaskar, Suju Rajan. 455-463 [doi]
• Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and TreeChen-Yu Lee, Patrick W. Gallagher, Zhuowen Tu. 464-472 [doi]
• Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCAChun-Liang Li, Hsuan-Tien Lin, Chi-Jen Lu. 473-481 [doi]
• Quantization based Fast Inner Product SearchRuiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha. 482-490 [doi]
• An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex MinimizationXingguo Li, Tuo Zhao, Raman Arora, Han Liu, Mingyi Hong. 491-499 [doi]
• Learning Structured Low-Rank Representation via Matrix FactorizationJie Shen, Ping Li 0001. 500-509 [doi]
• A PAC RL Algorithm for Episodic POMDPsZhaohan Daniel Guo, Shayan Doroudi, Emma Brunskill. 510-518 [doi]
• Large Scale Distributed Semi-Supervised Learning Using Streaming ApproximationSujith Ravi, Qiming Diao. 519-528 [doi]
• Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical ModelsCalvin McCarter, Seyoung Kim. 528-537 [doi]
• Graph Connectivity in Noisy Sparse Subspace ClusteringYining Wang, Yu-Xiang Wang, Aarti Singh. 538-546 [doi]
• The Nonparametric Kernel Bayes SmootherYu Nishiyama, Amir Afsharinejad, Shunsuke Naruse, Byron Boots, Le Song. 547-555 [doi]
• Universal Models of Multivariate Temporal Point ProcessesAsela Gunawardana, Christopher Meek. 556-563 [doi]
• Online Relative Entropy Policy Search using Reproducing Kernel Hilbert Space EmbeddingsZhitang Chen, Pascal Poupart, Yanhui Geng. 573-581 [doi]
• Relationship between PreTraining and Maximum Likelihood Estimation in Deep Boltzmann MachinesMuneki Yasuda. 582-590 [doi]
• Enumerating Equivalence Classes of Bayesian Networks using EC GraphsEunice Yuh-Jie Chen, Arthur Choi, Adnan Darwiche. 591-599 [doi]
• Low-Rank and Sparse Structure Pursuit via Alternating MinimizationQuanquan Gu, Zhaoran Wang, Han Liu. 600-609 [doi]
• NuC-MKL: A Convex Approach to Non Linear Multiple Kernel LearningEli A. Meirom, Pavel Kisilev. 610-619 [doi]
• Tractable and Scalable Schatten Quasi-Norm Approximations for Rank MinimizationFanhua Shang, Yuanyuan Liu, James Cheng. 620-629 [doi]
• Fast Dictionary Learning with a Smoothed Wasserstein LossAntoine Rolet, Marco Cuturi, Gabriel Peyré. 630-638 [doi]
• New Resistance Distances with Global Information on Large GraphsCanh Hao Nguyen, Hiroshi Mamitsuka. 639-647 [doi]
• Batch Bayesian Optimization via Local PenalizationJavier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence. 648-657 [doi]
• Nonparametric Budgeted Stochastic Gradient DescentTrung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung. 654-572 [doi]
• Learning Relationships between Data Obtained IndependentlyAlexandra Carpentier, Teresa Schlueter. 658-666 [doi]
• Fast and Scalable Structural SVM with Slack RescalingHeejin Choi, Ofer Meshi, Nathan Srebro. 667-675 [doi]
• Probabilistic Approximate Least-SquaresSimon Bartels, Philipp Hennig. 676-684 [doi]
• Approximate Inference Using DC Programming For Collective Graphical ModelsDuc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon. 685-693 [doi]
• Sequential Inference for Deep Gaussian ProcessYali Wang, Marcus A. Brubaker, Brahim Chaib-draa, Raquel Urtasun. 694-703 [doi]
• Variational TemperingStephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David M. Blei. 704-712 [doi]
• On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel SystemYi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing. 713-722 [doi]
• Scalable MCMC for Mixed Membership Stochastic BlockmodelsWenzhe Li, Sungjin Ahn, Max Welling. 723-731 [doi]
• Non-Stationary Gaussian Process Regression with Hamiltonian Monte CarloMarkus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki. 732-740 [doi]
• A Deep Generative Deconvolutional Image ModelYunchen Pu, Xin Yuan, Andrew Stevens, Chunyuan Li, Lawrence Carin. 741-750 [doi]
• Distributed Multi-Task LearningJialei Wang, Mladen Kolar, Nathan Srebro. 751-760 [doi]
• A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian ModelsRishit Sheth, Roni Khardon. 761-769 [doi]
• Learning Probabilistic Submodular Diversity Models Via Noise Contrastive EstimationSebastian Tschiatschek, Josip Djolonga, Andreas Krause 0001. 770-779 [doi]
• Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with Ordered L1-NormSangkyun Lee, Damian Brzyski, Malgorzata Bogdan. 780-789 [doi]
• GLASSES: Relieving The Myopia Of Bayesian OptimisationJavier González, Michael A. Osborne, Neil D. Lawrence. 790-799 [doi]
• Stochastic Variational Inference for the HDP-HMMAonan Zhang, San Gultekin, John Paisley. 800-808 [doi]
• Stochastic Neural Networks with Monotonic Activation FunctionsSiamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner. 809-818 [doi]
• (Bandit) Convex Optimization with Biased Noisy Gradient OraclesXiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári. 819-828 [doi]
• Variational Gaussian Copula InferenceShaobo Han, Xuejun Liao, David B. Dunson, Lawrence Carin. 829-838 [doi]
• Low-Rank Approximation of Weighted Tree AutomataGuillaume Rabusseau, Borja Balle, Shay B. Cohen. 839-847 [doi]
• Accelerating Online Convex Optimization via Adaptive PredictionMehryar Mohri, Scott Yang. 848-856 [doi]
• Scalable geometric density estimationYe Wang, Antonio Canale, David B. Dunson. 857-865 [doi]
• Model-based Co-clustering for High Dimensional Sparse DataAghiles Salah, Nicoleta Rogovschi, Mohamed Nadif. 866-874 [doi]
• DUAL-LOCO: Distributing Statistical Estimation Using Random ProjectionsChristina Heinze, Brian McWilliams, Nicolai Meinshausen. 875-883 [doi]
• High Dimensional Bayesian Optimization via Restricted Projection Pursuit ModelsChun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider. 884-892 [doi]
• On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov GamesJulien Pérolat, Bilal Piot, Bruno Scherrer, Olivier Pietquin. 893-901 [doi]
• Semi-Supervised Learning with Adaptive Spectral TransformHanxiao Liu, Yiming Yang. 902-910 [doi]
• Pseudo-Marginal Slice SamplingIain Murray, Matthew Graham. 911-919 [doi]
• How to Learn a Graph from Smooth SignalsVassilis Kalofolias. 920-929 [doi]
• Ordered Weighted L1 Regularized Regression with Strongly Correlated Covariates: Theoretical AspectsMário A. T. Figueiredo, Robert D. Nowak. 930-938 [doi]
• Pareto Front Identification from Stochastic Bandit FeedbackPeter Auer, Chao-Kai Chiang, Ronald Ortner, Madalina M. Drugan. 939-947 [doi]
• Sketching, Embedding and Dimensionality Reduction in Information Theoretic SpacesAmirali Abdullah, Ravi Kumar, Andrew McGregor, Sergei Vassilvitskii, Suresh Venkatasubramanian. 948-956 [doi]
• AdaDelay: Delay Adaptive Distributed Stochastic OptimizationSuvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola. 957-965 [doi]
• Exponential Stochastic Cellular Automata for Massively Parallel InferenceManzil Zaheer, Michael Wick, Jean-Baptiste Tristan, Alexander J. Smola, Guy L. Steele Jr.. 966-975 [doi]
• Globally Sparse Probabilistic PCAPierre-Alexandre Mattei, Charles Bouveyron, Pierre Latouche. 976-984 [doi]
• Provable Bayesian Inference via Particle Mirror DescentBo Dai, Niao He, Hanjun Dai, Le Song. 985-994 [doi]
• Unsupervised Feature Selection by Preserving Stochastic NeighborsXiaokai Wei, Philip S. Yu. 995-1003 [doi]
• Improved Learning Complexity in Combinatorial Pure Exploration BanditsVictor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter L. Bartlett. 1004-1012 [doi]
• Scalable Gaussian Processes for Characterizing Multidimensional Change SurfacesWilliam Herlands, Andrew Gordon Wilson, Hannes Nickisch, Seth Flaxman, Daniel B. Neill, Wilbert Van Panhuis, Eric P. Xing. 1013-1021 [doi]
• Optimization as Estimation with Gaussian Processes in Bandit SettingsZi Wang, Bolei Zhou, Stefanie Jegelka. 1022-1031 [doi]
• A Convex Surrogate Operator for General Non-Modular Loss FunctionsJiaqian Yu, Matthew B. Blaschko. 1032-1041 [doi]
• Inference for High-dimensional Exponential Family Graphical ModelsJialei Wang, Mladen Kolar. 1042-1050 [doi]
• Bridging the Gap between Stochastic Gradient MCMC and Stochastic OptimizationChangyou Chen, David E. Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin. 1051-1060 [doi]
• Fitting Spectral Decay with the \(k\)-Support NormAndrew M. McDonald, Massimiliano Pontil, Dimitris Stamos. 1061-1069 [doi]
• Early Stopping as Nonparametric Variational InferenceDavid K. Duvenaud, Dougal Maclaurin, Ryan P. Adams. 1070-1077 [doi]
• Bayesian Nonparametric Kernel-LearningJunier B. Oliva, Avinava Dubey, Andrew Gordon Wilson, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing. 1078-1086 [doi]
• Tight Variational Bounds via Random Projections and I-ProjectionsLun-Kai Hsu, Tudor Achim, Stefano Ermon. 1087-1095 [doi]
• Bethe Learning of Graphical Models via MAP DecodingKui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara. 1096-1104 [doi]
• Determinantal Regularization for Ensemble Variable SelectionVeronika Rocková, Gemma E. Moran, Edward I. George. 1105-1113 [doi]
• Scalable and Sound Low-Rank Tensor LearningHao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, Dale Schuurmans. 1114-1123 [doi]
• Non-negative Matrix Factorization for Discrete Data with Hierarchical Side-InformationChangwei Hu, Piyush Rai, Lawrence Carin. 1124-1132 [doi]
• Topic-Based Embeddings for Learning from Large Knowledge GraphsChangwei Hu, Piyush Rai, Lawrence Carin. 1133-1141 [doi]
• Consistently Estimating Markov Chains with Noisy Aggregate DataGarrett Bernstein, Daniel Sheldon. 1142-1150 [doi]
• Unwrapping ADMM: Efficient Distributed Computing via Transpose ReductionTom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre. 1151-1158 [doi]
• Improper Deep KernelsUri Heinemann, Roi Livni, Elad Eban, Gal Elidan, Amir Globerson. 1159-1167 [doi]
• Unbounded Bayesian Optimization via RegularizationBobak Shahriari, Alexandre Bouchard-Côté, Nando de Freitas. 1168-1176 [doi]
• Non-Gaussian Component Analysis with Log-Density Gradient EstimationHiroaki Sasaki, Gang Niu, Masashi Sugiyama. 1177-1185 [doi]
• Online Learning with Noisy Side ObservationsTomás Kocák, Gergely Neu, Michal Valko. 1186-1194 [doi]
• Black-Box Policy Search with Probabilistic ProgramsJan-Willem van de Meent, Brooks Paige, David Tolpin, Frank Wood. 1195-1204 [doi]
• Efficient Bregman Projections onto the Permutahedron and Related PolytopesCong Han Lim, Stephen J. Wright. 1205-1213 [doi]
• On Searching for Generalized Instrumental VariablesBenito van der Zander, Maciej Liskiewicz. 1214-1222 [doi]
• Provable Tensor Methods for Learning Mixtures of Generalized Linear ModelsHanie Sedghi, Majid Janzamin, Anima Anandkumar. 1223-1231 [doi]
• Controlling Bias in Adaptive Data Analysis Using Information TheoryDaniel Russo 0001, James Zou. 1232-1240 [doi]
• A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization GuaranteesJean-Francis Roy, Mario Marchand, François Laviolette. 1241-1249 [doi]
• Graph Sparsification Approaches for Laplacian SmoothingVeeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani. 1250-1259 [doi]
• Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column GenerationIan En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar. 1260-1269 [doi]
• Robust Covariate Shift RegressionXiangli Chen, Mathew Monfort, Anqi Liu, Brian D. Ziebart. 1270-1279 [doi]
• On Lloyd's Algorithm: New Theoretical Insights for Clustering in PracticeCheng Tang, Claire Monteleoni. 1280-1289 [doi]
• Towards Stability and Optimality in Stochastic Gradient DescentPanos Toulis, Dustin Tran, Edoardo M. Airoldi. 1290-1298 [doi]
• Communication Efficient Distributed Agnostic BoostingShang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau. 1299-1307 [doi]
• Private Causal InferenceMatt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger. 1308-1317 [doi]
• Parallel Markov Chain Monte Carlo via Spectral ClusteringGuillaume W. Basse, Aaron Smith, Natesh Pillai. 1318-1327 [doi]
• Efficient Sampling for k-Determinantal Point ProcessesChengtao Li, Stefanie Jegelka, Suvrit Sra. 1328-1337 [doi]
• A Fast and Reliable Policy Improvement AlgorithmYasin Abbasi-Yadkori, Peter L. Bartlett, Stephen J. Wright. 1338-1346 [doi]
• Learning Sigmoid Belief Networks via Monte Carlo Expectation MaximizationZhao Song, Ricardo Henao, David E. Carlson, Lawrence Carin. 1347-1355 [doi]
• Active Learning Algorithms for Graphical Model SelectionGautam Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong Hyuk Park. 1356-1364 [doi]
• Streaming Kernel Principal Component AnalysisMina Ghashami, Daniel J. Perry, Jeff M. Phillips. 1365-1374 [doi]
• Back to the Future: Radial Basis Function Networks RevisitedQichao Que, Mikhail Belkin. 1375-1383 [doi]
• Cut Pursuit: Fast Algorithms to Learn Piecewise Constant FunctionsLoïc Landrieu, Guillaume Obozinski. 1384-1393 [doi]
• Loss Bounds and Time Complexity for Speed PriorsDaniel Filan, Jan Leike, Marcus Hutter. 1394-1402 [doi]
• NYTRO: When Subsampling Meets Early StoppingRaffaello Camoriano, Tomás Angles, Alessandro Rudi, Lorenzo Rosasco. 1403-1411 [doi]
• Randomization and The Pernicious Effects of Limited Budgets on Auction ExperimentsGuillaume W. Basse, Hossein Azari Soufiani, Diane Lambert. 1412-1420 [doi]
• Spectral M-estimation with Applications to Hidden Markov ModelsDustin Tran, Minjae Kim, Finale Doshi-Velez. 1421-1430 [doi]
• Chained Gaussian ProcessesAlan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence. 1431-1440 [doi]
• Multiresolution Matrix CompressionNedelina Teneva, Pramod Kaushik Mudrakarta, Risi Kondor. 1441-1449 [doi]
• Supervised Neighborhoods for Distributed Nonparametric RegressionAdam Bloniarz, Ameet Talwalkar, Bin Yu 0001, Christopher Wu. 1450-1459 [doi]
• Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace EstimationDejiao Zhang, Laura Balzano. 1460-1468 [doi]
• Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product NetworksAbdullah Rashwan, Han Zhao, Pascal Poupart. 1469-1477 [doi]
• Mondrian Forests for Large-Scale Regression when Uncertainty MattersBalaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh. 1478-1487 [doi]
• Online (and Offline) Robust PCA: Novel Algorithms and Performance GuaranteesJinchun Zhan, Brian Lois, Han Guo, Namrata Vaswani. 1488-1496 [doi]
• Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex OptimizationYan Kaganovsky, Ikenna Odinaka, David E. Carlson, Lawrence Carin. 1497-1505 [doi]
• Discriminative Structure Learning of Arithmetic CircuitsAmirmohammad Rooshenas, Daniel Lowd. 1506-1514 [doi]
• One Scan 1-Bit Compressed SensingPing Li 0001. 1515-1523 [doi]