Table of Contents

• Convex Optimization over Intersection of Simple Sets: improved Convergence Rate Guarantees via an Exact Penalty ApproachAchintya Kundu, Francis Bach, Chiranjib Bhattacharyya. [doi]
• The Geometry of Random FeaturesKrzysztof Choromanski, Mark Rowland, Tamás Sarlós, Vikas Sindhwani, Richard E. Turner, Adrian Weller. 1-9 [doi]
• Gauged Mini-Bucket Elimination for Approximate InferenceSungsoo Ahn, Michael Chertkov, Jinwoo Shin, Adrian Weller. 10-19 [doi]
• A Fast Algorithm for Separated Sparsity via Perturbed LagrangiansAleksander Madry, Slobodan Mitrovic, Ludwig Schmidt. 20-28 [doi]
• An Analysis of Categorical Distributional Reinforcement LearningMark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh. 29-37 [doi]
• Combinatorial Preconditioners for Proximal Algorithms on GraphsThomas Möllenhoff, Zhenzhang Ye, Tao Wu 0006, Daniel Cremers. 38-47 [doi]
• Growth-Optimal Portfolio Selection under CVaR ConstraintsGuy Uziel, Ran El-Yaniv. 48-57 [doi]
• Accelerated Stochastic Power IterationPeng Xu, Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré. 58-67 [doi]
• Multi-scale Nystrom MethodWoosang Lim, Rundong Du, Bo Dai, Kyomin Jung, Le Song, Haesun Park. 68-76 [doi]
• Making Tree Ensembles Interpretable: A Bayesian Model Selection ApproachSatoshi Hara, Kohei Hayashi. 77-85 [doi]
• Mixed Membership Word Embeddings for Computational Social ScienceJames R. Foulds. 86-95 [doi]
• Fast Threshold Tests for Detecting DiscriminationEmma Pierson, Sam Corbett-Davies, Sharad Goel. 96-105 [doi]
• Iterative Supervised Principal ComponentsJuho Piironen, Aki Vehtari. 106-114 [doi]
• Iterative Spectral Method for Alternative ClusteringChieh Wu, Stratis Ioannidis, Mario Sznaier, Xiangyu Li, David R. Kaeli, Jennifer G. Dy. 115-123 [doi]
• Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-meansDennis Forster, Jörg Lücke. 124-132 [doi]
• Parallelised Bayesian Optimisation via Thompson SamplingKirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos. 133-142 [doi]
• On the challenges of learning with inference networks on sparse, high-dimensional dataRahul G. Krishnan, Dawen Liang, Matthew D. Hoffman. 143-151 [doi]
• Post Selection Inference with KernelsMakoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi. 152-160 [doi]
• On how complexity affects the stability of a predictorJoel Ratsaby. 161-167 [doi]
• On Truly Block Eigensolvers via Riemannian OptimizationZhiqiang Xu, Xin Gao. 168-177 [doi]
• Layerwise Systematic Scan: Deep Boltzmann Machines and BeyondHeng Guo 0001, Kaan Kara, Ce Zhang. 178-187 [doi]
• IHT dies hard: Provable accelerated Iterative Hard ThresholdingRajiv Khanna, Anastasios Kyrillidis. 188-198 [doi]
• Finding Global Optima in Nonconvex Stochastic Semidefinite Optimization with Variance ReductionJinshan Zeng, Ke Ma, Yuan Yao. 199-207 [doi]
• Outlier Detection and Robust Estimation in Nonparametric RegressionDehan Kong, Howard D. Bondell, Weining Shen. 208-216 [doi]
• Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier AnalysisLuca Ambrogioni, Eric Maris. 217-225 [doi]
• AdaGeo: Adaptive Geometric Learning for Optimization and SamplingGabriele Abbati, Alessandra Tosi, Michael A. Osborne, Seth R. Flaxman. 226-234 [doi]
• Online Learning with Non-Convex Losses and Non-Stationary RegretXiand Gao, Xiaobo Li, Shuzhong Zhang. 235-243 [doi]
• Learning Determinantal Point Processes in Sublinear TimeChristophe Dupuy, Francis Bach. 244-257 [doi]
• Nonlinear Structured Signal Estimation in High Dimensions via Iterative Hard ThresholdingKaiqing Zhang, Zhuoran Yang, Zhaoran Wang. 258-268 [doi]
• Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysisHiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra. 269-278 [doi]
• Online Boosting Algorithms for Multi-label RankingYoung-Hun Jung, Ambuj Tewari. 279-287 [doi]
• Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and ApplicationsSijia Liu 0001, Jie Chen, Pin-Yu Chen, Alfred Hero. 288-297 [doi]
• High-Dimensional Bayesian Optimization via Additive Models with Overlapping GroupsPaul Rolland, Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher. 298-307 [doi]
• Robust Active Label CorrectionJan Kremer, Fei Sha, Christian Igel. 308-316 [doi]
• Factorial HMMs with Collapsed Gibbs Sampling for Optimizing Long-term HIV TherapyAmit Gruber, Chen Yanover, Tal El-Hay, Anders Sönnerborg, Vanni Borghi, Francesca Incardona, Yaara Goldschmidt. 317-326 [doi]
• Optimal Submodular Extensions for Marginal EstimationPankaj Pansari, Chris Russell, M. Pawan Kumar. 327-335 [doi]
• Semi-Supervised Learning with Competitive Infection ModelsNir Rosenfeld, Amir Globerson. 336-346 [doi]
• Discriminative Learning of Prediction IntervalsNir Rosenfeld, Yishay Mansour, Elad Yom-Tov. 347-355 [doi]
• Topic Compositional Neural Language ModelWenlin Wang, Zhe Gan, Wenqi Wang, Dinghan Shen, Jiaji Huang, Wei Ping, Sanjeev Satheesh, Lawrence Carin. 356-365 [doi]
• Learning Priors for InvarianceEric T. Nalisnick, Padhraic Smyth. 366-375 [doi]
• Optimal Cooperative InferenceScott Cheng-Hsin Yang, Yue Yu, Arash Givchi, Pei Wang, Wai Keen Vong, Patrick Shafto. 376-385 [doi]
• Stochastic Multi-armed Bandits in Constant SpaceDavid Liau, Zhao Song, Eric Price, Ger Yang. 386-394 [doi]
• Matrix completability analysis via graph k-connectivityDehua Cheng, Natali Ruchansky, Yan Liu. 395-403 [doi]
• FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient MethodsXiang Cheng, Fred Roosta, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney. 404-414 [doi]
• Multi-view Metric Learning in Vector-valued Kernel SpacesRiikka Huusari, Hachem Kadri, Cécile Capponi. 415-424 [doi]
• Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid DataWilliam Herlands, Edward McFowland, Andrew Gordon Wilson, Daniel B. Neill. 425-434 [doi]
• Dropout as a Low-Rank Regularizer for Matrix FactorizationJacopo Cavazza, Pietro Morerio, Benjamin D. Haeffele, Connor Lane, Vittorio Murino, René Vidal. 435-444 [doi]
• A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex RegularizerTianbao Yang, Zhe Li, Lijun Zhang. 445-453 [doi]
• Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated VariablesMasaaki Takada, Taiji Suzuki, Hironori Fujisawa. 454-463 [doi]
• Boosting Variational Inference: an Optimization PerspectiveFrancesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch. 464-472 [doi]
• Personalized and Private Peer-to-Peer Machine LearningAurélien Bellet, Rachid Guerraoui, Mahsa Taziki, Marc Tommasi. 473-481 [doi]
• Tensor Regression Meets Gaussian ProcessesRose Yu, Guangyu Li, Yan Liu. 482-490 [doi]
• A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida RegularizationEmanuel Laude, Tao Wu, Daniel Cremers. 491-499 [doi]
• Medoids in Almost-Linear Time via Multi-Armed BanditsVivek Kumar Bagaria, Govinda M. Kamath, Vasilis Ntranos, Martin J. Zhang, David Tse. 500-509 [doi]
• Regional Multi-Armed BanditsZhiyang Wang, Ruida Zhou, Cong Shen. 510-518 [doi]
• Nearly second-order optimality of online joint detection and estimation via one-sample update schemesYang Cao, Liyan Xie, Yao Xie, Huan Xu. 519-528 [doi]
• Sum-Product-Quotient NetworksOr Sharir, Amnon Shashua. 529-537 [doi]
• Exploiting Strategy-Space Diversity for Batch Bayesian OptimizationSunil Gupta, Alistair Shilton, Santu Rana, Svetha Venkatesh. 538-547 [doi]
• Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing MethodsStéphan Clémençon, François Portier. 548-556 [doi]
• Group invariance principles for causal generative modelsMichel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing. 557-565 [doi]
• A Provable Algorithm for Learning Interpretable Scoring SystemsNataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker. 566-574 [doi]
• Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian ProcessesHyunjik Kim, Yee Whye Teh. 575-584 [doi]
• Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision DiagramsShinsaku Sakaue, Masakazu Ishihata, Shin-ichi Minato. 585-594 [doi]
• Transfer Learning on fMRI DatasetsHejia Zhang, Po-Hsuan Chen, Peter J. Ramadge. 595-603 [doi]
• An Optimization Approach to Learning Falling Rule ListsChaofan Chen, Cynthia Rudin. 604-612 [doi]
• Catalyst for Gradient-based Nonconvex OptimizationCourtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaïd Harchaoui. 613-622 [doi]
• Benefits from Superposed Hawkes ProcessesHongteng Xu, Dixin Luo, Xu Chen, Lawrence Carin. 623-631 [doi]
• Nonparametric Preference CompletionJulian Katz-Samuels, Clayton Scott. 632-641 [doi]
• Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network trainingMathieu Sinn, Ambrish Rawat. 642-651 [doi]
• Efficient and principled score estimation with Nyström kernel exponential familiesDougal J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton. 652-660 [doi]
• Symmetric Variational Autoencoder and Connections to Adversarial LearningLiqun Chen, Shuyang Dai, Yunchen Pu, Erjin Zhou, Chunyuan Li, Qinliang Su, Changyou Chen, Lawrence Carin. 661-669 [doi]
• Few-shot Generative Modelling with Generative Matching NetworksSergey Bartunov, Dmitry P. Vetrov. 670-678 [doi]
• Nonlinear Weighted Finite AutomataTianyu Li, Guillaume Rabusseau, Doina Precup. 679-688 [doi]
• Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process ModelsHugh Salimbeni, Stefanos Eleftheriadis, James Hensman. 689-697 [doi]
• Variational inference for the multi-armed contextual banditIñigo Urteaga, Chris Wiggins. 698-706 [doi]
• Tracking the gradients using the Hessian: A new look at variance reducing stochastic methodsRobert M. Gower, Nicolas Le Roux, Francis Bach. 707-715 [doi]
• Subsampling for Ridge Regression via Regularized Volume SamplingMichal Derezinski, Manfred K. Warmuth. 716-725 [doi]
• Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train DecompositionPavel Izmailov, Alexander Novikov, Dmitry Kropotov. 726-735 [doi]
• Batch-Expansion Training: An Efficient Optimization FrameworkMichal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer. 736-744 [doi]
• Batched Large-scale Bayesian Optimization in High-dimensional SpacesZi Wang, Clement Gehring, Pushmeet Kohli, Stefanie Jegelka. 745-754 [doi]
• Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time SeriesFeras Saad, Vikash Mansinghka. 755-764 [doi]
• Stochastic Three-Composite Convex Minimization with a Linear OperatorRenbo Zhao, Volkan Cevher. 765-774 [doi]
• Direct Learning to Rank And RerankCynthia Rudin, Yining Wang. 775-783 [doi]
• One-shot Coresets: The Case of k-ClusteringOlivier Bachem, Mario Lucic, Silvio Lattanzi. 784-792 [doi]
• Random Warping Series: A Random Features Method for Time-Series EmbeddingLingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock. 793-802 [doi]
• Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGDSanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh, Parijat Dube, Priya Nagpurkar. 803-812 [doi]
• Variational Inference based on Robust DivergencesFutoshi Futami, Issei Sato, Masashi Sugiyama. 813-822 [doi]
• Variational Rejection SamplingAditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon. 823-832 [doi]
• Best arm identification in multi-armed bandits with delayed feedbackAditya Grover, Todor Markov, Peter Attia, Norman Jin, Nicolas Perkins, Bryan Cheong, Michael Chen, Zi Yang, Stephen Harris, William Chueh, Stefano Ermon. 833-842 [doi]
• A fully adaptive algorithm for pure exploration in linear banditsLiyuan Xu, Junya Honda, Masashi Sugiyama. 843-851 [doi]
• Contextual Bandits with Stochastic ExpertsRajat Sen, Karthikeyan Shanmugam, Sanjay Shakkottai. 852-861 [doi]
• Human Interaction with Recommendation SystemsSven Schmit, Carlos Riquelme. 862-870 [doi]
• Community Detection in Hypergraphs: Optimal Statistical Limit and Efficient AlgorithmsI Eli Chien, Chung-Yi Lin, I-Hsiang Wang. 871-879 [doi]
• Smooth and Sparse Optimal TransportMathieu Blondel, Vivien Seguy, Antoine Rolet. 880-889 [doi]
• Robust Maximization of Non-Submodular ObjectivesIlija Bogunovic, Junyao Zhao, Volkan Cevher. 890-899 [doi]
• Cause-Effect Inference by Comparing Regression ErrorsPatrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf. 900-909 [doi]
• Tree-based Bayesian Mixture Model for Competing RisksAlexis Bellot, Mihaela van der Schaar. 910-918 [doi]
• Actor-Critic Fictitious Play in Simultaneous Move Multistage GamesJulien Pérolat, Bilal Piot, Olivier Pietquin. 919-928 [doi]
• Random Subspace with Trees for Feature Selection Under Memory ConstraintsAntonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts. 929-937 [doi]
• Conditional independence testing based on a nearest-neighbor estimator of conditional mutual informationJakob Runge. 938-947 [doi]
• Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network StructuresTomi Silander, Janne Leppä-aho, Elias Jääsaari, Teemu Roos. 948-957 [doi]
• Variational Sequential Monte CarloChristian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei. 968-977 [doi]
• Statistically Efficient Estimation for Non-Smooth Probability DensitiesMasaaki Imaizumi, Takanori Maehara, Yuichi Yoshida. 978-987 [doi]
• SDCA-Powered Inexact Dual Augmented Lagrangian Method for Fast CRF LearningXu Hu, Guillaume Obozinski. 988-997 [doi]
• Generalized Concomitant Multi-Task Lasso for Sparse Multimodal RegressionMathurin Massias, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon. 998-1007 [doi]
• Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative ModelsAtsushi Nitanda, Taiji Suzuki. 1008-1016 [doi]
• Statistical Sparse Online Regression: A Diffusion Approximation PerspectiveJianqing Fan, Wenyan Gong, Chris Junchi Li, Qiang Sun. 1017-1026 [doi]
• Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient OptimizationFanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida. 1027-1036 [doi]
• Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic ProgramsLawrence Murray, Daniel Lundën, Jan Kudlicka, David Broman, Thomas B. Schön. 1037-1046 [doi]
• Learning to Round for Discrete Labeling ProblemsPritish Mohapatra, C. V. Jawahar, M. Pawan Kumar. 1047-1056 [doi]
• Approximate ranking from pairwise comparisonsReinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright. 1057-1066 [doi]
• Semi-Supervised Prediction-Constrained Topic ModelsMichael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez. 1067-1076 [doi]
• A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed LoopYichen Wang, Evangelos Theodorou, Apurv Verma, Le Song. 1077-1086 [doi]
• Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time AlgorithmsPan Xu, Tianhao Wang, Quanquan Gu. 1087-1096 [doi]
• A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix RecoveryXiao Zhang, Lingxiao Wang, Quanquan Gu. 1097-1107 [doi]
• Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence ModelingHongyi Ding, Mohammad Emtiyaz Khan, Issei Sato, Masashi Sugiyama. 1108-1116 [doi]
• Factor Analysis on a GraphMasayuki Karasuyama, Hiroshi Mamitsuka. 1117-1126 [doi]
• Crowdclustering with Partition LabelsJunxiang Chen, Yale Chang, Peter J. Castaldi, Michael H. Cho, Brian Hobbs, Jennifer G. Dy. 1127-1136 [doi]
• Learning Structural Weight Uncertainty for Sequential Decision-MakingRuiyi Zhang, Chunyuan Li, Changyou Chen, Lawrence Carin. 1137-1146 [doi]
• Towards Memory-Friendly Deterministic Incremental Gradient MethodJiahao Xie, Hui Qian, Zebang Shen, Chao Zhang. 1147-1156 [doi]
• Optimality of Approximate Inference Algorithms on Stable InstancesHunter Lang, David Sontag, Aravindan Vijayaraghavan. 1157-1166 [doi]
• Bayesian Approaches to Distribution RegressionHo Chung Leon Law, Dougal J. Sutherland, Dino Sejdinovic, Seth R. Flaxman. 1167-1176 [doi]
• Submodularity on Hypergraphs: From Sets to SequencesMarko Mitrovic, Moran Feldman, Andreas Krause 0001, Amin Karbasi. 1177-1184 [doi]
• Provable Estimation of the Number of Blocks in Block ModelsBowei Yan, Purnamrita Sarkar, Xiuyuan Cheng. 1185-1194 [doi]
• Differentially Private Regression with Gaussian ProcessesMichael Thomas Smith, Mauricio A. Álvarez, Max Zwiessele, Neil D. Lawrence. 1195-1203 [doi]
• Adaptive balancing of gradient and update computation times using global geometry and approximate subproblemsSai Praneeth Reddy Karimireddy, Sebastian U. Stich, Martin Jaggi. 1204-1213 [doi]
• VAE with a VampPriorJakub M. Tomczak, Max Welling. 1214-1223 [doi]
• Structured Factored Inference for Probabilistic ProgrammingAvi Pfeffer, Brian E. Ruttenberg, William Kretschmer, Alison O'Connor. 1224-1232 [doi]
• A Generic Approach for Escaping Saddle pointsSashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis Bach, Ruslan Salakhutdinov, Alexander J. Smola. 1233-1242 [doi]
• Policy Evaluation and Optimization with Continuous TreatmentsNathan Kallus, Angela Zhou. 1243-1251 [doi]
• Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential EquationsAlan Lazarus, Dirk Husmeier, Theodore Papamarkou. 1252-1260 [doi]
• Why Adaptively Collected Data Have Negative Bias and How to Correct for ItXinkun Nie, Xiaoying Tian, Jonathan Taylor, James Zou. 1261-1269 [doi]
• Sparse Linear Isotonic ModelsSheng Chen, Arindam Banerjee. 1270-1279 [doi]
• Robustness of classifiers to uniform $\ell_p$ and Gaussian noiseJean-Yves Franceschi, Alhussein Fawzi, Omar Fawzi. 1280-1288 [doi]
• Nested CRP with Hawkes-Gaussian ProcessesXi Tan, Vinayak Rao, Jennifer Neville. 1289-1298 [doi]
• Sketching for Kronecker Product Regression and P-splinesHuaian Diao, Zhao Song, Wen Sun, David P. Woodruff. 1299-1308 [doi]
• Multimodal Prediction and Personalization of Photo Edits with Deep Generative ModelsArdavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson 0002, Ryan P. Adams. 1309-1317 [doi]
• Cheap Checking for Cloud Computing: Statistical Analysis via Annotated Data StreamsChris Hickey, Graham Cormode. 1318-1326 [doi]
• Minimax Reconstruction Risk of Convolutional Sparse Dictionary LearningShashank Singh 0005, Barnabás Póczos, Jian Ma. 1327-1336 [doi]
• Kernel Conditional Exponential FamilyMichael Arbel, Arthur Gretton. 1337-1346 [doi]
• Linear Stochastic Approximation: How Far Does Constant Step-Size and Iterate Averaging Go?Chandrashekar Lakshminarayanan, Csaba Szepesvári. 1347-1355 [doi]
• Stochastic Zeroth-order Optimization in High DimensionsYining Wang, Simon S. Du, Sivaraman Balakrishnan, Aarti Singh. 1356-1365 [doi]
• Teacher Improves Learning by Selecting a Training SubsetYuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu 0001. 1366-1375 [doi]
• Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance EstimationPenporn Koanantakool, Alnur Ali, Ariful Azad, Aydin Buluç, Dmitriy Morozov, Leonid Oliker, Katherine A. Yelick, Sang-Yun Oh. 1376-1386 [doi]
• Robust Vertex Enumeration for Convex Hulls in High DimensionsPranjal Awasthi, Bahman Kalantari, Yikai Zhang. 1387-1396 [doi]
• Fast generalization error bound of deep learning from a kernel perspectiveTaiji Suzuki. 1397-1406 [doi]
• Product Kernel Interpolation for Scalable Gaussian ProcessesJacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson. 1407-1416 [doi]
• Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix EstimationMohammadreza Soltani, Chinmay Hegde. 1417-1426 [doi]
• Scalable Generalized Dynamic Topic ModelsPatrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt. 1427-1435 [doi]
• Bayesian Structure Learning for Dynamic Brain ConnectivityMichael Riis Andersen, Ole Winther, Lars Kai Hansen, Russell A. Poldrack, Oluwasanmi Koyejo. 1436-1446 [doi]
• Large Scale Empirical Risk Minimization via Truncated Adaptive Newton MethodMark Eisen, Aryan Mokhtari, Alejandro Ribeiro. 1447-1455 [doi]
• Frank-Wolfe Splitting via Augmented Lagrangian MethodGauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien. 1456-1465 [doi]
• Learning linear structural equation models in polynomial time and sample complexityAsish Ghoshal, Jean Honorio. 1466-1475 [doi]
• Convergence diagnostics for stochastic gradient descent with constant learning rateJerry Chee, Panos Toulis. 1476-1485 [doi]
• Learning Sparse Polymatrix Games in Polynomial Time and Sample ComplexityAsish Ghoshal, Jean Honorio. 1486-1494 [doi]
• Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex OptimizationSeung-Jean Kim, Johan Lim, Joong-Ho Won. 1495-1504 [doi]
• Stochastic algorithms for entropy-regularized optimal transport problemsBrahim Khalil Abid, Robert M. Gower. 1505-1512 [doi]
• Plug-in Estimators for Conditional Expectations and ProbabilitiesSteffen Grünewälder. 1513-1521 [doi]
• Factorized Recurrent Neural Architectures for Longer Range DependenceFrancois Belletti, Alex Beutel, Sagar Jain, Ed Huai-hsin Chi. 1522-1530 [doi]
• On the Statistical Efficiency of Compositional Nonparametric PredictionYixi Xu, Jean Honorio, Xiao Wang. 1531-1539 [doi]
• Metrics for Deep Generative ModelsNutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick van der Smagt. 1540-1550 [doi]
• Combinatorial Penalties: Which structures are preserved by convex relaxations?Marwa El Halabi, Francis Bach, Volkan Cevher. 1551-1560 [doi]
• Generalized Binary Search For Split-Neighborly ProblemsStephen Mussmann, Percy Liang. 1561-1569 [doi]
• Intersection-Validation: A Method for Evaluating Structure Learning without Ground TruthJussi Viinikka, Ralf Eggeling, Mikko Koivisto. 1570-1578 [doi]
• On Statistical Optimality of Variational BayesDebdeep Pati, Anirban Bhattacharya, Yun Yang. 1579-1588 [doi]
• Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed SystemsJason Ge, Zhaoran Wang, Mengdi Wang, Han Liu. 1589-1598 [doi]
• Online Regression with Partial Information: Generalization and Linear ProjectionShinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi. 1599-1607 [doi]
• Learning Generative Models with Sinkhorn DivergencesAude Genevay, Gabriel Peyré, Marco Cuturi. 1608-1617 [doi]
• Reparameterizing the Birkhoff Polytope for Variational Permutation InferenceScott W. Linderman, Gonzalo E. Mena, Hal Cooper, Liam Paninski, John P. Cunningham. 1618-1627 [doi]
• Achieving the time of 1-NN, but the accuracy of k-NNLirong Xue, Samory Kpotufe. 1628-1636 [doi]
• Efficient Weight Learning in High-Dimensional Untied MLNsMohammad Khan Al Farabi, Somdeb Sarkhel, Deepak Venugopal. 1637-1645 [doi]
• Learning with Complex Loss Functions and ConstraintsHarikrishna Narasimhan. 1646-1654 [doi]
• Solving lp-norm regularization with tensor kernelsSaverio Salzo, Lorenzo Rosasco, Johan Suykens. 1655-1663 [doi]
• Weighted Tensor Decomposition for Learning Latent Variables with Partial DataOmer Gottesman, Weiwei Pan, Finale Doshi-Velez. 1664-1672 [doi]
• Multi-objective Contextual Bandit Problem with Similarity InformationEralp Turgay, Doruk Öner, Cem Tekin. 1673-1681 [doi]
• Turing: Composable inference for probabilistic programmingHong Ge, Kai Xu, Zoubin Ghahramani. 1682-1690 [doi]
• Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model StructureBeilun Wang, Arshdeep Sekhon, Yanjun Qi. 1691-1700 [doi]
• Data-Efficient Reinforcement Learning with Probabilistic Model Predictive ControlSanket Kamthe, Marc Peter Deisenroth. 1701-1710 [doi]
• Approximate Bayesian Computation with Kullback-Leibler Divergence as Data DiscrepancyBai Jiang. 1711-1721 [doi]
• Practical Bayesian optimization in the presence of outliersRuben Martinez-Cantin, Kevin Tee, Michael McCourt. 1722-1731 [doi]
• Competing with Automata-based Expert SequencesMehryar Mohri, Scott Yang. 1732-1740 [doi]
• Reducing Crowdsourcing to Graphon Estimation, StatisticallyDevavrat Shah, Christina E. Lee. 1741-1750 [doi]
• Robust Locally-Linear Controllable EmbeddingErshad Banijamali, Rui Shu, Mohammad Ghavamzadeh, Hung Hai Bui, Ali Ghodsi 0001. 1751-1759 [doi]
• Combinatorial Semi-Bandits with KnapsacksKarthik Abinav Sankararaman, Aleksandrs Slivkins. 1760-1770 [doi]
• Structured Optimal TransportDavid Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka. 1771-1780 [doi]
• Graphical Models for Non-Negative Data Using Generalized Score MatchingShiqing Yu, Mathias Drton, Ali Shojaie. 1781-1790 [doi]
• Asynchronous Doubly Stochastic Group Regularized LearningBin Gu, Zhouyuan Huo, Heng Huang. 1791-1800 [doi]
• Convergence of Value Aggregation for Imitation LearningChing-An Cheng, Byron Boots. 1801-1809 [doi]
• Inference in Sparse Graphs with Pairwise Measurements and Side InformationDylan J. Foster, Karthik Sridharan, Daniel Reichman 0001. 1810-1818 [doi]
• Parallel and Distributed MCMC via Shepherding DistributionsArkabandhu Chowdhury, Christopher M. Jermaine. 1819-1827 [doi]
• The Power Mean Laplacian for Multilayer Graph ClusteringPedro Mercado, Antoine Gautier, Francesco Tudisco, Matthias Hein 0001. 1828-1838 [doi]
• Adaptive Sampling for Coarse RankingSumeet Katariya, Lalit Jain, Nandana Sengupta, James Evans, Robert Nowak. 1839-1848 [doi]
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