Table of Contents

• No Oops, You Won't Do It Again: Mechanisms for Self-correction in CrowdsourcingNihar B. Shah, Dengyong Zhou. 1-10 [doi]
• Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational IssuesNihar B. Shah, Sivaraman Balakrishnan, Aditya Guntuboyina, Martin J. Wainwright. 11-20 [doi]
• Uprooting and Rerooting Graphical ModelsAdrian Weller. 21-29 [doi]
• A Deep Learning Approach to Unsupervised Ensemble LearningUri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph T. Chang, Yuval Kluger. 30-39 [doi]
• Revisiting Semi-Supervised Learning with Graph EmbeddingsZhilin Yang, William W. Cohen, Ruslan Salakhutdinov. 40-48 [doi]
• Guided Cost Learning: Deep Inverse Optimal Control via Policy OptimizationChelsea Finn, Sergey Levine, Pieter Abbeel. 49-58 [doi]
• Diversity-Promoting Bayesian Learning of Latent Variable ModelsPengtao Xie, Jun Zhu, Eric P. Xing. 59-68 [doi]
• Additive Approximations in High Dimensional Nonparametric Regression via the SALSAKirthevasan Kandasamy, Yaoliang Yu. 69-78 [doi]
• Hawkes Processes with Stochastic ExcitationsYoung Lee, Kar Wai Lim, Cheng Soon Ong. 79-88 [doi]
• Data-driven Rank Breaking for Efficient Rank AggregationAshish Khetan, Sewoong Oh. 89-98 [doi]
• Dropout distillationSamuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder. 99-107 [doi]
• Metadata-conscious anonymous messagingGiulia C. Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath. 108-116 [doi]
• The Teaching Dimension of Linear LearnersJi Liu, Xiaojin Zhu 0001, Hrag Ohannessian. 117-126 [doi]
• Truthful Univariate EstimatorsIoannis Caragiannis, Ariel D. Procaccia, Nisarg Shah 0001. 127-135 [doi]
• Why Regularized Auto-Encoders learn Sparse Representation?Devansh Arpit, Yingbo Zhou, Hung Q. Ngo 0001, Venu Govindaraju. 136-144 [doi]
• k-variates++: more pluses in the k-means++Richard Nock, Raphaël Canyasse, Roksana Boreli, Frank Nielsen. 145-154 [doi]
• Multi-Player Bandits - a Musical Chairs ApproachJonathan Rosenski, Ohad Shamir, Liran Szlak. 155-163 [doi]
• The Information SieveGreg Ver Steeg, Aram Galstyan. 164-172 [doi]
• Deep Speech 2 : End-to-End Speech Recognition in English and MandarinDario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich Elsen, Jesse Engel, Linxi Fan, Christopher Fougner, Awni Y. Hannun, Billy Jun, Tony Han, Patrick LeGresley, Xiangang Li, Libby Lin, Sharan Narang, Andrew Y. Ng, Sherjil Ozair, Ryan Prenger, Sheng Qian, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Chong Wang, Yi Wang, Zhiqian Wang, Bo Xiao, Yan Xie, Dani Yogatama, Jun Zhan, Zhenyao Zhu. 173-182 [doi]
• On the Consistency of Feature Selection With Lasso for Non-linear TargetsYue Zhang, Weihong Guo, Soumya Ray. 183-191 [doi]
• Minimum Regret Search for Single- and Multi-Task OptimizationJan Hendrik Metzen. 192-200 [doi]
• CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and AccuracyRan Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin E. Lauter, Michael Naehrig, John Wernsing. 201-210 [doi]
• The Variational Nystrom method for large-scale spectral problemsMax Vladymyrov, Miguel Á. Carreira-Perpiñán. 211-220 [doi]
• Multi-Bias Non-linear Activation in Deep Neural NetworksHongyang Li, Wanli Ouyang, Xiaogang Wang. 221-229 [doi]
• Asymmetric Multi-task Learning based on Task Relatedness and ConfidenceGiwoong Lee, Eunho Yang, Sung Ju Hwang. 230-238 [doi]
• Accurate Robust and Efficient Error Estimation for Decision TreesLixin Fan. 239-247 [doi]
• Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and ConvexityOhad Shamir. 248-256 [doi]
• Convergence of Stochastic Gradient Descent for PCAOhad Shamir. 257-265 [doi]
• Dealbreaker: A Nonlinear Latent Variable Model for Educational DataAndrew S. Lan, Tom Goldstein, Richard G. Baraniuk, Christoph Studer. 266-275 [doi]
• A Kernelized Stein Discrepancy for Goodness-of-fit TestsQiang Liu, Jason D. Lee, Michael I. Jordan. 276-284 [doi]
• Variable Elimination in the Fourier DomainYexiang Xue, Stefano Ermon, Ronan Le Bras, Carla P. Gomes, Bart Selman. 285-294 [doi]
• Low-Rank Matrix Approximation with StabilityDongsheng Li, Chao Chen, Qin Lv, Junchi Yan, Li Shang, Stephen M. Chu. 295-303 [doi]
• Linking losses for density ratio and class-probability estimationAditya Krishna Menon, Cheng Soon Ong. 304-313 [doi]
• Stochastic Variance Reduction for Nonconvex OptimizationSashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola. 314-323 [doi]
• Hierarchical Variational ModelsRajesh Ranganath, Dustin Tran, David M. Blei. 324-333 [doi]
• Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data StreamsRoy J. Adams, Nazir Saleheen, Edison Thomaz, Abhinav Parate, Santosh Kumar, Benjamin M. Marlin. 334-343 [doi]
• Binary embeddings with structured hashed projectionsAnna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun. 344-353 [doi]
• A Variational Analysis of Stochastic Gradient AlgorithmsStephan Mandt, Matthew D. Hoffman, David M. Blei. 354-363 [doi]
• Adaptive Sampling for SGD by Exploiting Side InformationSiddharth Gopal. 364-372 [doi]
• Learning from Multiway Data: Simple and Efficient Tensor RegressionRose Yu, Yan Liu. 373-381 [doi]
• A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression ModelsTrong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low. 382-391 [doi]
• Online Stochastic Linear Optimization under One-bit FeedbackLijun Zhang 0005, Tianbao Yang, Rong Jin, Yichi Xiao, Zhi-Hua Zhou. 392-401 [doi]
• Adaptive Algorithms for Online Convex Optimization with Long-term ConstraintsRodolphe Jenatton, Jim Huang, Cédric Archambeau. 402-411 [doi]
• Actively Learning Hemimetrics with Applications to Eliciting User PreferencesAdish Singla, Sebastian Tschiatschek, Andreas Krause 0001. 412-420 [doi]
• Learning Simple Algorithms from ExamplesWojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus. 421-429 [doi]
• Learning Physical Intuition of Block Towers by ExampleAdam Lerer, Sam Gross, Rob Fergus. 430-438 [doi]
• Structure Learning of Partitioned Markov NetworksSong Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu. 439-448 [doi]
• Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy GradientTianbao Yang, Lijun Zhang 0005, Rong Jin, Jinfeng Yi. 449-457 [doi]
• Beyond CCA: Moment Matching for Multi-View ModelsAnastasia Podosinnikova, Francis R. Bach, Simon Lacoste-Julien. 458-467 [doi]
• Fast methods for estimating the Numerical rank of large matricesShashanka Ubaru, Yousef Saad. 468-477 [doi]
• Unsupervised Deep Embedding for Clustering AnalysisJunyuan Xie, Ross B. Girshick, Ali Farhadi. 478-487 [doi]
• Efficient Private Empirical Risk Minimization for High-dimensional LearningShiva Prasad Kasiviswanathan, Hongxia Jin. 488-497 [doi]
• Parameter Estimation for Generalized Thurstone Choice ModelsMilan Vojnovic, Se-Young Yun. 498-506 [doi]
• Large-Margin Softmax Loss for Convolutional Neural NetworksWeiyang Liu, YanDong Wen, Zhiding Yu, Meng Yang. 507-516 [doi]
• A Random Matrix Approach to Echo-State Neural NetworksRomain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, Harry Sevi. 517-525 [doi]
• Supervised and Semi-Supervised Text Categorization using LSTM for Region EmbeddingsRie Johnson, Tong Zhang 0001. 526-534 [doi]
• Optimality of Belief Propagation for Crowdsourced ClassificationJungseul Ok, Sewoong Oh, Jinwoo Shin, Yung Yi. 535-544 [doi]
• Stability of Controllers for Gaussian Process Forward ModelsJulia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Anne Romer, Henner Schmidt, Jan Peters 0001. 545-554 [doi]
• Learning privately from multiparty dataJihun Hamm, Yingjun Cao, Mikhail Belkin. 555-563 [doi]
• Network MorphismTao Wei, Changhu Wang, Yong Rui, Chang Wen Chen. 564-572 [doi]
• A Kronecker-factored approximate Fisher matrix for convolution layersRoger B. Grosse, James Martens. 573-582 [doi]
• Experimental Design on a Budget for Sparse Linear Models and ApplicationsSathya N. Ravi, Vamsi K. Ithapu, Sterling C. Johnson, Vikas Singh. 583-592 [doi]
• Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMsAnton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet Kumar Dokania, Simon Lacoste-Julien. 593-602 [doi]
• Exact Exponent in Optimal Rates for CrowdsourcingChao Gao, Yu Lu, Dengyong Zhou. 603-611 [doi]
• Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image ClassificationYuting Zhang, Kibok Lee, Honglak Lee. 612-621 [doi]
• Online Low-Rank Subspace Clustering by Basis Dictionary PursuitJie Shen, Ping Li, Huan Xu. 622-631 [doi]
• A Self-Correcting Variable-Metric Algorithm for Stochastic OptimizationFrank Curtis. 632-641 [doi]
• Stochastic Quasi-Newton Langevin Monte CarloUmut Simsekli, Roland Badeau, A. Taylan Cemgil, Gaël Richard. 642-651 [doi]
• Doubly Robust Off-policy Value Evaluation for Reinforcement LearningNan Jiang, Lihong Li. 652-661 [doi]
• Fast Rate Analysis of Some Stochastic Optimization AlgorithmsChao Qu, Huan Xu, Chong Jin Ong. 662-670 [doi]
• Fast k-Nearest Neighbour Search via Dynamic Continuous IndexingKe Li, Jitendra Malik. 671-679 [doi]
• Smooth Imitation Learning for Online Sequence PredictionHoang Minh Le 0002, Andrew Kang, Yisong Yue, Peter Carr 0001. 680-688 [doi]
• Community Recovery in Graphs with LocalityYuxin Chen, Govinda M. Kamath, Changho Suh, David Tse. 689-698 [doi]
• Variance Reduction for Faster Non-Convex OptimizationZeyuan Allen Zhu, Elad Hazan. 699-707 [doi]
• Loss factorization, weakly supervised learning and label noise robustnessGiorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni. 708-717 [doi]
• Analysis of Deep Neural Networks with Extended Data Jacobian MatrixShengjie Wang, Abdel-rahman Mohamed, Rich Caruana, Jeff A. Bilmes, Matthai Philipose, Matthew Richardson, Krzysztof Geras, Gregor Urban, Özlem Aslan. 718-726 [doi]
• Doubly Decomposing Nonparametric Tensor RegressionMasaaki Imaizumi, Kohei Hayashi. 727-736 [doi]
• Hyperparameter optimization with approximate gradientFabian Pedregosa. 737-746 [doi]
• SDCA without Duality, Regularization, and Individual ConvexityShai Shalev-Shwartz. 747-754 [doi]
• Heteroscedastic Sequences: Beyond GaussianityOren Anava, Shie Mannor. 755-763 [doi]
• A Neural Autoregressive Approach to Collaborative FilteringYin Zheng, Bangsheng Tang, Wenkui Ding, Hanning Zhou. 764-773 [doi]
• On the Quality of the Initial Basin in Overspecified Neural NetworksItay Safran, Ohad Shamir. 774-782 [doi]
• Primal-Dual Rates and CertificatesCelestine Dünner, Simone Forte, Martin Takác, Martin Jaggi. 783-792 [doi]
• Minimizing the Maximal Loss: How and WhyShai Shalev-Shwartz, Yonatan Wexler. 793-801 [doi]
• The Information-Theoretic Requirements of Subspace Clustering with Missing DataDaniel L. Pimentel-Alarcón, Robert D. Nowak. 802-810 [doi]
• Online Learning with Feedback Graphs Without the GraphsAlon Cohen, Tamir Hazan, Tomer Koren. 811-819 [doi]
• PAC learning of Probabilistic Automaton based on the Method of MomentsHadrien Glaude, Olivier Pietquin. 820-829 [doi]
• Estimating Structured Vector Autoregressive ModelsIgor Melnyk, Arindam Banerjee. 830-839 [doi]
• Mixing Rates for the Alternating Gibbs Sampler over Restricted Boltzmann Machines and FriendsChristopher Tosh. 840-849 [doi]
• Polynomial Networks and Factorization Machines: New Insights and Efficient Training AlgorithmsMathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda. 850-858 [doi]
• A New PAC-Bayesian Perspective on Domain AdaptationPascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant. 859-868 [doi]
• Correlation Clustering and Biclustering with Locally Bounded ErrorsGregory J. Puleo, Olgica Milenkovic. 869-877 [doi]
• PAC Lower Bounds and Efficient Algorithms for The Max \(K\)-Armed Bandit ProblemYahel David, Nahum Shimkin. 878-887 [doi]
• A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose EstimationMohamed Elhoseiny, Tarek El-Gaaly, Amr Bakry, Ahmed M. Elgammal. 888-897 [doi]
• BASC: Applying Bayesian Optimization to the Search for Global Minima on Potential Energy SurfacesShane Carr, Roman Garnett, Cynthia Lo. 898-907 [doi]
• On the Iteration Complexity of Oblivious First-Order Optimization AlgorithmsYossi Arjevani, Ohad Shamir. 908-916 [doi]
• Stochastic Variance Reduced Optimization for Nonconvex Sparse LearningXingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis D. Haupt. 917-925 [doi]
• Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank EstimationDavid P. Wipf. 926-935 [doi]
• Fast k-means with accurate boundsJames Newling, François Fleuret. 936-944 [doi]
• Boolean Matrix Factorization and Noisy Completion via Message PassingSiamak Ravanbakhsh, Barnabás Póczos, Russell Greiner. 945-954 [doi]
• Convolutional Rectifier Networks as Generalized Tensor DecompositionsNadav Cohen, Amnon Shashua. 955-963 [doi]
• Low-rank Solutions of Linear Matrix Equations via Procrustes FlowStephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, Ben Recht. 964-973 [doi]
• Anytime Exploration for Multi-armed Bandits using Confidence InformationKwang-Sung Jun, Robert D. Nowak. 974-982 [doi]
• Structured Prediction Energy NetworksDavid Belanger, Andrew McCallum. 983-992 [doi]
• L1-regularized Neural Networks are Improperly Learnable in Polynomial TimeYuchen Zhang, Jason D. Lee, Michael I. Jordan. 993-1001 [doi]
• Compressive Spectral ClusteringNicolas Tremblay, Gilles Puy, Rémi Gribonval, Pierre Vandergheynst. 1002-1011 [doi]
• Low-rank tensor completion: a Riemannian manifold preconditioning approachHiroyuki Kasai, Bamdev Mishra. 1012-1021 [doi]
• Provable Non-convex Phase Retrieval with Outliers: Median TruncatedWirtinger FlowHuishuai Zhang, Yuejie Chi, Yingbin Liang. 1022-1031 [doi]
• Estimating Maximum Expected Value through Gaussian ApproximationCarlo D'Eramo, Marcello Restelli, Alessandro Nuara. 1032-1040 [doi]
• Representational Similarity Learning with Application to Brain NetworksUrvashi Oswal, Christopher R. Cox, Matthew A. Lambon-Ralph, Timothy T. Rogers, Robert D. Nowak. 1041-1049 [doi]
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep LearningYarin Gal, Zoubin Ghahramani. 1050-1059 [doi]
• Generative Adversarial Text to Image SynthesisScott E. Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee. 1060-1069 [doi]
• Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression DataSandhya Prabhakaran, Elham Azizi, Ambrose Carr, Dana Pe'er. 1070-1079 [doi]
• Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex ObjectivesZeyuan Allen Zhu, Yang Yuan. 1080-1089 [doi]
• Sparse Parameter Recovery from Aggregated DataAvradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo. 1090-1099 [doi]
• Deep Structured Energy Based Models for Anomaly DetectionShuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang. 1100-1109 [doi]
• Even Faster Accelerated Coordinate Descent Using Non-Uniform SamplingZeyuan Allen Zhu, Zheng Qu, Peter Richtárik, Yang Yuan. 1110-1119 [doi]
• Unitary Evolution Recurrent Neural NetworksMartín Arjovsky, Amar Shah, Yoshua Bengio. 1120-1128 [doi]
• Markov Latent Feature ModelsAonan Zhang, John Paisley. 1129-1137 [doi]
• The Knowledge Gradient for Sequential Decision Making with Stochastic Binary FeedbacksYingfei Wang, Chu Wang, Warren B. Powell. 1138-1147 [doi]
• A Simple and Provable Algorithm for Sparse Diagonal CCAMegasthenis Asteris, Anastasios Kyrillidis, Oluwasanmi Koyejo, Russell A. Poldrack. 1148-1157 [doi]
• Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search MethodsHuikang Liu, Weijie Wu, Anthony Man-Cho So. 1158-1167 [doi]
• Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep NetworksDevansh Arpit, Yingbo Zhou, Bhargava Urala Kota, Venu Govindaraju. 1168-1176 [doi]
• Learning to Generate with MemoryChongxuan Li, Jun Zhu, Bo Zhang. 1177-1186 [doi]
• Learning End-to-end Video Classification with Rank-PoolingBasura Fernando, Stephen Gould. 1187-1196 [doi]
• Learning to Filter with Predictive State Inference MachinesWen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell. 1197-1205 [doi]
• A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row SamplingMostafa Rahmani, George K. Atia. 1206-1214 [doi]
• DCM Bandits: Learning to Rank with Multiple ClicksSumeet Katariya, Branislav Kveton, Csaba Szepesvári, Zheng Wen. 1215-1224 [doi]
• Train faster, generalize better: Stability of stochastic gradient descentMoritz Hardt, Ben Recht, Yoram Singer. 1225-1234 [doi]
• Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient AlgorithmJunpei Komiyama, Junya Honda, Hiroshi Nakagawa. 1235-1244 [doi]
• Contextual Combinatorial Cascading BanditsShuai Li, Baoxiang Wang, Shengyu Zhang, Wei Chen. 1245-1253 [doi]
• Conservative BanditsYifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvári. 1254-1262 [doi]
• Variance-Reduced and Projection-Free Stochastic OptimizationElad Hazan, Haipeng Luo. 1263-1271 [doi]
• Factored Temporal Sigmoid Belief Networks for Sequence LearningJiaming Song, Zhe Gan, Lawrence Carin. 1272-1281 [doi]
• False Discovery Rate Control and Statistical Quality Assessment of Annotators in Crowdsourced RankingQianqian Xu, Jiechao Xiong, Xiaochun Cao, Yuan Yao. 1282-1291 [doi]
• Strongly-Typed Recurrent Neural NetworksDavid Balduzzi, Muhammad Ghifary. 1292-1300 [doi]
• Distributed Clustering of Linear Bandits in Peer to Peer NetworksNathan Korda, Balázs Szörényi, Shuai Li. 1301-1309 [doi]
• Collapsed Variational Inference for Sum-Product NetworksHan Zhao, Tameem Adel, Geoff Gordon, Brandon Amos. 1310-1318 [doi]
• On the Analysis of Complex Backup Strategies in Monte Carlo Tree SearchPiyush Khandelwal, Elad Liebman, Scott Niekum, Peter Stone. 1319-1328 [doi]
• Benchmarking Deep Reinforcement Learning for Continuous ControlYan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel. 1329-1338 [doi]
• \(K\)-Means Clustering with Distributed DimensionsHu Ding, Yu Liu, Lingxiao Huang, Jian Li. 1339-1348 [doi]
• Texture Networks: Feed-forward Synthesis of Textures and Stylized ImagesDmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, Victor S. Lempitsky. 1349-1357 [doi]
• Fast Constrained Submodular Maximization: Personalized Data SummarizationBaharan Mirzasoleiman, Ashwinkumar Badanidiyuru, Amin Karbasi. 1358-1367 [doi]
• On the Statistical Limits of Convex RelaxationsZhaoran Wang, Quanquan Gu, Han Liu. 1368-1377 [doi]
• Ask Me Anything: Dynamic Memory Networks for Natural Language ProcessingAnkit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, Richard Socher. 1378-1387 [doi]
• Gossip Dual Averaging for Decentralized Optimization of Pairwise FunctionsIgor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon. 1388-1396 [doi]
• Solving Ridge Regression using Sketched Preconditioned SVRGAlon Gonen, Francesco Orabona, Shai Shalev-Shwartz. 1397-1405 [doi]
• Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and ControlPrashanth L. A., Cheng Jie, Michael Fu, Steve Marcus, Csaba Szepesvári. 1406-1415 [doi]
• Estimating Accuracy from Unlabeled Data: A Bayesian ApproachEmmanouil Antonios Platanios, Avinava Dubey, Tom M. Mitchell. 1416-1425 [doi]
• Non-negative Matrix Factorization under Heavy NoiseChiranjib Bhattacharyya, Navin Goyal, Ravindran Kannan, Jagdeep Pani. 1426-1434 [doi]
• Extreme F-measure Maximization using Sparse Probability EstimatesKalina Jasinska, Krzysztof Dembczynski, Róbert Busa-Fekete, Karlson Pfannschmidt, Timo Klerx, Eyke Hüllermeier. 1435-1444 [doi]
• Auxiliary Deep Generative ModelsLars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther. 1445-1453 [doi]
• Importance Sampling Tree for Large-scale Empirical ExpectationOlivier Canévet, Cijo Jose, François Fleuret. 1454-1462 [doi]
• Starting Small - Learning with Adaptive Sample SizesHadi Daneshmand, Aurélien Lucchi, Thomas Hofmann. 1463-1471 [doi]
• Deep Gaussian Processes for Regression using Approximate Expectation PropagationThang D. Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Richard E. Turner. 1472-1481 [doi]
• DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution RegressionJovana Mitrovic, Dino Sejdinovic, Yee Whye Teh. 1482-1491 [doi]
• Predictive Entropy Search for Multi-objective Bayesian OptimizationDaniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams. 1492-1501 [doi]
• Rich Component AnalysisRong Ge, James Zou. 1502-1510 [doi]
• Black-Box Alpha Divergence MinimizationJosé Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner. 1511-1520 [doi]
• One-Shot Generalization in Deep Generative ModelsDanilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra. 1521-1529 [doi]
• Optimal Classification with Multivariate LossesNagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit S. Dhillon. 1530-1538 [doi]
• A ranking approach to global optimizationCedric Malherbe, Emile Contal, Nicolas Vayatis. 1539-1547 [doi]
• Parallel and Distributed Block-Coordinate Frank-Wolfe AlgorithmsYu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing. 1548-1557 [doi]
• Autoencoding beyond pixels using a learned similarity metricAnders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther. 1558-1566 [doi]
• Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs SamplingChristopher De Sa, Christopher Ré, Kunle Olukotun. 1567-1576 [doi]
• Simultaneous Safe Screening of Features and Samples in Doubly Sparse ModelingAtsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano, Ichiro Takeuchi. 1577-1586 [doi]
• Anytime optimal algorithms in stochastic multi-armed banditsRémy Degenne, Vianney Perchet. 1587-1595 [doi]
• Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum DesignWilliam Hoiles, Mihaela van der Schaar. 1596-1604 [doi]
• On collapsed representation of hierarchical Completely Random MeasuresGaurav Pandey, Ambedkar Dukkipati. 1605-1613 [doi]
• From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label ClassificationAndré F. T. Martins, Ramón Fernández Astudillo. 1614-1623 [doi]
• Black-box Optimization with a PoliticianSébastien Bubeck, Yin Tat Lee. 1624-1631 [doi]
• Gaussian process nonparametric tensor estimator and its minimax optimalityHeishiro Kanagawa, Taiji Suzuki, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami. 1632-1641 [doi]
• No-Regret Algorithms for Heavy-Tailed Linear BanditsAndres Muñoz Medina, Scott Yang. 1642-1650 [doi]
• Extended and Unscented Kitchen SinksEdwin V. Bonilla, Daniel M. Steinberg, Alistair Reid 0001. 1651-1659 [doi]
• Matrix Eigen-decomposition via Doubly Stochastic Riemannian OptimizationZhiqiang Xu, Peilin Zhao, Jianneng Cao, Xiaoli Li 0001. 1660-1669 [doi]
• Recommendations as Treatments: Debiasing Learning and EvaluationTobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims. 1670-1679 [doi]
• ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit AdmissionJinsung Yoon, Ahmed M. Alaa, Scott Hu, Mihaela van der Schaar. 1680-1689 [doi]
• An optimal algorithm for the Thresholding Bandit ProblemAndrea Locatelli, Maurilio Gutzeit, Alexandra Carpentier. 1690-1698 [doi]
• Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient MatchingMu Niu, Simon Rogers, Maurizio Filippone, Dirk Husmeier. 1699-1707 [doi]
• Structured and Efficient Variational Deep Learning with Matrix Gaussian PosteriorsChristos Louizos, Max Welling. 1708-1716 [doi]
• Learning Granger Causality for Hawkes ProcessesHongteng Xu, Mehrdad Farajtabar, Hongyuan Zha. 1717-1726 [doi]
• Neural Variational Inference for Text ProcessingYishu Miao, Lei Yu, Phil Blunsom. 1727-1736 [doi]
• Dictionary Learning for Massive Matrix FactorizationArthur Mensch, Julien Mairal, Bertrand Thirion, Gaël Varoquaux. 1737-1746 [doi]
• Pixel Recurrent Neural NetworksAäron Van Den Oord, Nal Kalchbrenner, Koray Kavukcuoglu. 1747-1756 [doi]
• Why Most Decisions Are Easy in Tetris - And Perhaps in Other Sequential Decision Problems, As WellÖzgür Simsek, Simon Algorta, Amit Kothiyal. 1757-1765 [doi]
• Gaussian quadrature for matrix inverse forms with applicationsChengtao Li, Suvrit Sra, Stefanie Jegelka. 1766-1775 [doi]
• Train and Test Tightness of LP Relaxations in Structured PredictionOfer Meshi, Mehrdad Mahdavi, Adrian Weller, David Sontag. 1776-1785 [doi]
• Stochastic Optimization for Multiview Representation Learning using Partial Least SquaresRaman Arora, Poorya Mianjy, Teodor Marinov. 1786-1794 [doi]
• Hierarchical Compound Poisson FactorizationMehmet Emin Basbug, Barbara Engelhardt. 1795-1803 [doi]
• Opponent Modeling in Deep Reinforcement LearningHe He, Jordan L. Boyd-Graber. 1804-1813 [doi]
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