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ORCIDNIPS 2015: Montreal, Quebec, Canada
- Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, Roman Garnett:
Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada. 2015 - Nihar Bhadresh Shah, Denny Zhou:
Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing. 1-9 - Brendan van Rooyen, Aditya Krishna Menon, Robert C. Williamson:
Learning with Symmetric Label Noise: The Importance of Being Unhinged. 10-18 - Ibrahim M. Alabdulmohsin:
Algorithmic Stability and Uniform Generalization. 19-27 - Theodoros Tsiligkaridis, Keith W. Forsythe:
Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models. 28-36 - Xiaocheng Shang, Zhanxing Zhu, Benedict J. Leimkuhler, Amos J. Storkey:
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling. 37-45 - Huitong Qiu, Fang Han, Han Liu, Brian Caffo:
Robust Portfolio Optimization. 46-54 - Anna Choromanska, John Langford:
Logarithmic Time Online Multiclass prediction. 55-63 - Julian Yarkony, Charless C. Fowlkes:
Planar Ultrametrics for Image Segmentation. 64-72 - Cesc C. Park, Gunhee Kim:
Expressing an Image Stream with a Sequence of Natural Sentences. 73-81 - Xinghao Pan, Dimitris S. Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan:
Parallel Correlation Clustering on Big Graphs. 82-90 - Shaoqing Ren, Kaiming He, Ross B. Girshick, Jian Sun:
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. 91-99 - Ke Sun, Jun Wang, Alexandros Kalousis, Stéphane Marchand-Maillet:
Space-Time Local Embeddings. 100-108 - Qinqing Zheng, John D. Lafferty:
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements. 109-117 - Bryan D. He, Yisong Yue:
Smooth Interactive Submodular Set Cover. 118-126 - Jiajun Wu, Ilker Yildirim, Joseph J. Lim, Bill Freeman, Joshua B. Tenenbaum:
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning. 127-135 - Jamie Morgenstern, Tim Roughgarden:
On the Pseudo-Dimension of Nearly Optimal Auctions. 136-144 - Mijung Park, Gergo Bohner, Jakob H. Macke:
Unlocking neural population non-stationarities using hierarchical dynamics models. 145-153 - Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltán Szabó, Lars Buesing, Maneesh Sahani:
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM). 154-162 - Ayan Chakrabarti:
Color Constancy by Learning to Predict Chromaticity from Luminance. 163-171 - Lucas Maystre, Matthias Grossglauser:
Fast and Accurate Inference of Plackett-Luce Models. 172-180 - Maren Mahsereci, Philipp Hennig:
Probabilistic Line Searches for Stochastic Optimization. 181-189 - Armand Joulin, Tomás Mikolov:
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets. 190-198 - Adrià Recasens, Aditya Khosla, Carl Vondrick, Antonio Torralba:
Where are they looking? 199-207 - Tor Lattimore:
The Pareto Regret Frontier for Bandits. 208-216 - Andrea Montanari, Daniel Reichman, Ofer Zeitouni:
On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors. 217-225 - Jackson Gorham, Lester W. Mackey:
Measuring Sample Quality with Stein's Method. 226-234 - Yan Huang, Wei Wang, Liang Wang:
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution. 235-243 - Guillaume P. Dehaene, Simon Barthelmé:
Bounding errors of Expectation-Propagation. 244-252 - Miguel Á. Carreira-Perpiñán, Max Vladymyrov:
A fast, universal algorithm to learn parametric nonlinear embeddings. 253-261 - Leon A. Gatys, Alexander S. Ecker, Matthias Bethge:
Texture Synthesis Using Convolutional Neural Networks. 262-270 - Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon:
Extending Gossip Algorithms to Distributed Estimation of U-statistics. 271-279 - Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How:
Streaming, Distributed Variational Inference for Bayesian Nonparametrics. 280-288 - Carl Vondrick, Hamed Pirsiavash, Aude Oliva, Antonio Torralba:
Learning visual biases from human imagination. 289-297 - Ofer Meshi, Mehrdad Mahdavi, Alexander G. Schwing:
Smooth and Strong: MAP Inference with Linear Convergence. 298-306 - Masrour Zoghi, Zohar S. Karnin, Shimon Whiteson, Maarten de Rijke:
Copeland Dueling Bandits. 307-315 - Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry A. Wasserman:
Optimal Ridge Detection using Coverage Risk. 316-324 - Maksim Lapin, Matthias Hein, Bernt Schiele
:
Top-k Multiclass SVM. 325-333 - Philip S. Thomas, Scott Niekum, Georgios Theocharous, George Dimitri Konidaris:
Policy Evaluation Using the Ω-Return. 334-342 - Megasthenis Asteris, Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Orthogonal NMF through Subspace Exploration. 343-351 - Tian Lin, Jian Li, Wei Chen:
Stochastic Online Greedy Learning with Semi-bandit Feedbacks. 352-360 - Guosheng Lin, Chunhua Shen, Ian D. Reid, Anton van den Hengel:
Deeply Learning the Messages in Message Passing Inference. 361-369 - David Kappel, Stefan Habenschuss, Robert Legenstein, Wolfgang Maass:
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring. 370-378 - Huan Li, Zhouchen Lin:
Accelerated Proximal Gradient Methods for Nonconvex Programming. 379-387 - Abhisek Kundu, Petros Drineas, Malik Magdon-Ismail:
Approximating Sparse PCA from Incomplete Data. 388-396 - Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins:
Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations. 397-405 - Saurabh Paul, Malik Magdon-Ismail, Petros Drineas:
Column Selection via Adaptive Sampling. 406-414 - Pinghua Gong, Jieping Ye:
HONOR: Hybrid Optimization for NOn-convex Regularized problems. 415-423 - Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G. Berneshawi, Huimin Ma, Sanja Fidler, Raquel Urtasun:
3D Object Proposals for Accurate Object Class Detection. 424-432 - Huasen Wu, R. Srikant, Xin Liu, Chong Jiang:
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits. 433-441 - Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry P. Vetrov:
Tensorizing Neural Networks. 442-450 - Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson:
Parallelizing MCMC with Random Partition Trees. 451-459 - Po-Hsuan Chen, Janice Chen, Yaara Yeshurun, Uri Hasson, James V. Haxby, Peter J. Ramadge:
A Reduced-Dimension fMRI Shared Response Model. 460-468 - Chicheng Zhang, Jimin Song, Kamalika Chaudhuri, Kevin C. Chen:
Spectral Learning of Large Structured HMMs for Comparative Epigenomics. 469-477 - Xia Qu, Prashant Doshi:
Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability. 478-486 - Sida Wang, Arun Tejasvi Chaganty, Percy Liang:
Estimating Mixture Models via Mixtures of Polynomials. 487-495 - Simon Lacoste-Julien, Martin Jaggi:
On the Global Linear Convergence of Frank-Wolfe Optimization Variants. 496-504 - Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, Jascha Sohl-Dickstein:
Deep Knowledge Tracing. 505-513 - Anastasia Podosinnikova, Francis R. Bach, Simon Lacoste-Julien:
Rethinking LDA: Moment Matching for Discrete ICA. 514-522 - Sohail Bahmani, Justin K. Romberg:
Efficient Compressive Phase Retrieval with Constrained Sensing Vectors. 523-531 - Rahul G. Krishnan, Simon Lacoste-Julien, David A. Sontag:
Barrier Frank-Wolfe for Marginal Inference. 532-540 - Vitaly Kuznetsov, Mehryar Mohri:
Learning Theory and Algorithms for Forecasting Non-stationary Time Series. 541-549 - Dinesh Ramasamy, Upamanyu Madhow:
Compressive spectral embedding: sidestepping the SVD. 550-558 - Tuo Zhao, Zhaoran Wang, Han Liu:
A Nonconvex Optimization Framework for Low Rank Matrix Estimation. 559-567 - Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David M. Blei:
Automatic Variational Inference in Stan. 568-576 - Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, Yoshua Bengio:
Attention-Based Models for Speech Recognition. 577-585 - Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar:
Closed-form Estimators for High-dimensional Generalized Linear Models. 586-594 - Róbert Busa-Fekete, Balázs Szörényi, Krzysztof Dembczynski, Eyke Hüllermeier:
Online F-Measure Optimization. 595-603 - Balázs Szörényi, Róbert Busa-Fekete, Adil Paul, Eyke Hüllermeier:
Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach. 604-612 - Alexander Kirillov, Dmytro Shlezinger, Dmitry P. Vetrov, Carsten Rother, Bogdan Savchynskyy:
M-Best-Diverse Labelings for Submodular Energies and Beyond. 613-621 - Janne H. Korhonen, Pekka Parviainen:
Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number. 622-630 - Gunwoong Park, Garvesh Raskutti:
Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring. 631-639 - Marylou Gabrié, Eric W. Tramel, Florent Krzakala:
Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy. 640-648 - Xiang Zhang, Junbo Jake Zhao, Yann LeCun:
Character-level Convolutional Networks for Text Classification. 649-657 - Ehsan Adeli-Mosabbeb, Kim-Han Thung, Le An, Feng Shi, Dinggang Shen:
Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis. 658-666 - Jean-Bastien Grill, Michal Valko, Rémi Munos:
Black-box optimization of noisy functions with unknown smoothness. 667-675 - Emmanuel Abbe, Colin Sandon:
Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters. 676-684 - Sixin Zhang, Anna Choromanska, Yann LeCun:
Deep learning with Elastic Averaging SGD. 685-693 - Naoto Ohsaka, Yuichi Yoshida:
Monotone k-Submodular Function Maximization with Size Constraints. 694-702 - Chicheng Zhang, Kamalika Chaudhuri:
Active Learning from Weak and Strong Labelers. 703-711 - Weiwei Liu, Ivor W. Tsang:
On the Optimality of Classifier Chain for Multi-label Classification. 712-720 - Kush Bhatia, Prateek Jain, Purushottam Kar:
Robust Regression via Hard Thresholding. 721-729 - Kush Bhatia, Himanshu Jain, Purushottam Kar, Manik Varma, Prateek Jain:
Sparse Local Embeddings for Extreme Multi-label Classification. 730-738 - Yuxin Chen, Emmanuel J. Candès:
Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems. 739-747 - Peter Schulam, Suchi Saria:
A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure. 748-756 - Chao Qu, Huan Xu:
Subspace Clustering with Irrelevant Features via Robust Dantzig Selector. 757-765 - Megasthenis Asteris, Dimitris S. Papailiopoulos, Anastasios Kyrillidis, Alexandros G. Dimakis:
Sparse PCA via Bipartite Matchings. 766-774 - Ahmed El Alaoui, Michael W. Mahoney:
Fast Randomized Kernel Ridge Regression with Statistical Guarantees. 775-783 - Oren Anava, Elad Hazan, Shie Mannor:
Online Learning for Adversaries with Memory: Price of Past Mistakes. 784-792 - Anqi Wu, Il Memming Park, Jonathan W. Pillow:
Convolutional spike-triggered covariance analysis for neural subunit models. 793-801 - Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, Wang-chun Woo:
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. 802-810 - Eugène Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
GAP Safe screening rules for sparse multi-task and multi-class models. 811-819 - Takashi Takenouchi, Takafumi Kanamori:
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces. 820-828 - James Robert Lloyd, Zoubin Ghahramani:
Statistical Model Criticism using Kernel Two Sample Tests. 829-837 - Peter A. Flach, Meelis Kull:
Precision-Recall-Gain Curves: PR Analysis Done Right. 838-846 - Tasuku Soma, Yuichi Yoshida:
A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice. 847-855 - Mathias Berglund, Tapani Raiko, Mikko Honkala, Leo Kärkkäinen, Akos Vetek, Juha Karhunen:
Bidirectional Recurrent Neural Networks as Generative Models. 856-864 - Zheng Qu, Peter Richtárik, Tong Zhang:
Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling. 865-873 - Justin Domke:
Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets. 874-882 - Minhyung Cho, Chandra Shekhar Dhir, Jaehyung Lee:
Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks. 883-891 - Vladimir Vovk, Ivan Petej, Valentina Fedorova:
Large-scale probabilistic predictors with and without guarantees of validity. 892-900 - Jimmy S. J. Ren, Li Xu, Qiong Yan, Wenxiu Sun:
Shepard Convolutional Neural Networks. 901-909 - Reshad Hosseini, Suvrit Sra:
Matrix Manifold Optimization for Gaussian Mixtures. 910-918 - Rie Johnson, Tong Zhang:
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding. 919-927 - Akihiro Kishimoto, Radu Marinescu, Adi Botea:
Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models. 928-936 - Ming Liang, Xiaolin Hu, Bo Zhang:
Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling. 937-945 - David B. Smith, Vibhav Gogate:
Bounding the Cost of Search-Based Lifted Inference. 946-954 - Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltán Szabó, Arthur Gretton:
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. 955-963 - Tor Lattimore, Koby Crammer, Csaba Szepesvári:
Linear Multi-Resource Allocation with Semi-Bandit Feedback. 964-972 - Kevin Ellis, Armando Solar-Lezama, Joshua B. Tenenbaum:
Unsupervised Learning by Program Synthesis. 973-981 - Ralph Bourdoukan, Sophie Denève:
Enforcing balance allows local supervised learning in spiking recurrent networks. 982-990 - Yining Wang, Hsiao-Yu Fish Tung, Alexander J. Smola, Anima Anandkumar:
Fast and Guaranteed Tensor Decomposition via Sketching. 991-999 - Yining Wang, Yu-Xiang Wang, Aarti Singh:
Differentially private subspace clustering. 1000-1008 - Prateek Jain, Nagarajan Natarajan, Ambuj Tewari:
Predtron: A Family of Online Algorithms for General Prediction Problems. 1009-1017 - Fredrik D. Johansson, Ankani Chattoraj, Chiranjib Bhattacharyya, Devdatt P. Dubhashi:
Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization. 1018-1026 - Guillaume Papa, Stéphan Clémençon, Aurélien Bellet:
SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk. 1027-1035 - Wei Cao, Jian Li, Yufei Tao, Zhize Li:
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs. 1036-1044 - Sebastian Bitzer, Stefan J. Kiebel:
The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions. 1045-1053 - Tianyang Li, Adarsh Prasad, Pradeep Ravikumar:
Fast Classification Rates for High-dimensional Gaussian Generative Models. 1054-1062 - Gustavo Malkomes, Matt J. Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley:
Fast Distributed k-Center Clustering with Outliers on Massive Data. 1063-1071 - Kwang-Sung Jun, Xiaojin Zhu, Timothy T. Rogers, Zhuoran Yang, Ming Yuan:
Human Memory Search as Initial-Visit Emitting Random Walk. 1072-1080 - Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng:
Non-convex Statistical Optimization for Sparse Tensor Graphical Model. 1081-1089 - Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi:
Convergence Rates of Active Learning for Maximum Likelihood Estimation. 1090-1098 - Jimei Yang, Scott E. Reed, Ming-Hsuan Yang, Honglak Lee:
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis. 1099-1107 - Pascal Vincent, Alexandre de Brébisson, Xavier Bouthillier:
Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets. 1108-1116 - Steven K. Esser, Rathinakumar Appuswamy, Paul Merolla, John V. Arthur, Dharmendra S. Modha:
Backpropagation for Energy-Efficient Neuromorphic Computing. 1117-1125 - Prateek Jain, Ambuj Tewari:
Alternating Minimization for Regression Problems with Vector-valued Outputs. 1126-1134 - Song Han, Jeff Pool, John Tran, William J. Dally:
Learning both Weights and Connections for Efficient Neural Network. 1135-1143 - Bharath K. Sriperumbudur, Zoltán Szabó:
Optimal Rates for Random Fourier Features. 1144-1152 - James McInerney, Rajesh Ranganath, David M. Blei:
The Population Posterior and Bayesian Modeling on Streams. 1153-1161 - François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne:
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. 1162-1170 - Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam Shazeer:
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks. 1171-1179 - Suriya Gunasekar, Arindam Banerjee, Joydeep Ghosh:
Unified View of Matrix Completion under General Structural Constraints. 1180-1188 - Pratik Jawanpuria, Maksim Lapin, Matthias Hein, Bernt Schiele:
Efficient Output Kernel Learning for Multiple Tasks. 1189-1197 - Michael C. Hughes, William T. Stephenson, Erik B. Sudderth:
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models. 1198-1206 - Maxim Rabinovich, Elaine Angelino, Michael I. Jordan:
Variational Consensus Monte Carlo. 1207-1215 - Murat A. Erdogdu:
Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma. 1216-1224 - Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya P. Razenshteyn, Ludwig Schmidt:
Practical and Optimal LSH for Angular Distance. 1225-1233 - Ross Goroshin, Michaël Mathieu, Yann LeCun:
Learning to Linearize Under Uncertainty. 1234-1242 - Sébastien Bubeck, Ronen Eldan, Joseph Lehec:
Finite-Time Analysis of Projected Langevin Monte Carlo. 1243-1251 - Scott E. Reed, Yi Zhang, Yuting Zhang, Honglak Lee:
Deep Visual Analogy-Making. 1252-1260 - Alaa Saade, Florent Krzakala, Lenka Zdeborová:
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation. 1261-1269 - Kent Quanrud, Daniel Khashabi:
Online Learning with Adversarial Delays. 1270-1278 - Jie Wang, Jieping Ye:
Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection. 1279-1287 - Sungsoo Ahn, Sejun Park, Michael Chertkov, Jinwoo Shin:
Minimum Weight Perfect Matching via Blossom Belief Propagation. 1288-1296 - Jaya Kawale, Hung Hai Bui, Branislav Kveton, Long Tran-Thanh, Sanjay Chawla:
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation. 1297-1305 - Ruoyu Sun, Mingyi Hong:
Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems. 1306-1314 - Timothy Kopp, Parag Singla, Henry A. Kautz:
Lifted Symmetry Detection and Breaking for MAP Inference. 1315-1323 - Jason D. Lee, Yuekai Sun, Jonathan E. Taylor:
Evaluating the statistical significance of biclusters. 1324-1332 - Jiaji Huang, Qiang Qiu, Guillermo Sapiro, A. Robert Calderbank:
Discriminative Robust Transformation Learning. 1333-1341 - Elias Bareinboim, Andrew Forney, Judea Pearl:
Bandits with Unobserved Confounders: A Causal Approach. 1342-1350 - Akshay Balsubramani, Yoav Freund:
Scalable Semi-Supervised Aggregation of Classifiers. 1351-1359 - Yifan Wu, András György, Csaba Szepesvári:
Online Learning with Gaussian Payoffs and Side Observations. 1360-1368 - Christian Borgs, Jennifer T. Chayes, Adam D. Smith:
Private Graphon Estimation for Sparse Graphs. 1369-1377 - Qing Sun, Dhruv Batra:
SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals. 1378-1386 - Kai Fan, Ziteng Wang, Jeffrey M. Beck, James T. Kwok, Katherine A. Heller:
Fast Second Order Stochastic Backpropagation for Variational Inference. 1387-1395 - Cameron Musco, Christopher Musco:
Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition. 1396-1404 - Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada, Takeshi Yamada:
Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions. 1405-1413 - Amir Dezfouli, Edwin V. Bonilla:
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods. 1414-1422 - Siddhartha Banerjee, Peter Lofgren:
Fast Bidirectional Probability Estimation in Markov Models. 1423-1431 - Qiang Liu, John W. Fisher III, Alexander Ihler:
Probabilistic Variational Bounds for Graphical Models. 1432-1440 - Ryan Giordano, Tamara Broderick, Michael I. Jordan:
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes. 1441-1449 - Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvári:
Combinatorial Cascading Bandits. 1450-1458 - Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvári:
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path. 1459-1467 - Aviv Tamar, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor:
Policy Gradient for Coherent Risk Measures. 1468-1476 - Tomer Koren, Kfir Y. Levy:
Fast Rates for Exp-concave Empirical Risk Minimization. 1477-1485 - Emily L. Denton, Soumith Chintala, Arthur Szlam, Rob Fergus:
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. 1486-1494 - Seunghoon Hong, Hyeonwoo Noh, Bohyung Han:
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation. 1495-1503 - Yann N. Dauphin, Harm de Vries, Yoshua Bengio:
Equilibrated adaptive learning rates for non-convex optimization. 1504-1512 - Dominik Rothenhäusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen:
BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions. 1513-1521 - Yinlam Chow, Aviv Tamar, Shie Mannor, Marco Pavone:
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach. 1522-1530 - Sorathan Chaturapruek, John C. Duchi, Christopher Ré:
Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care. 1531-1539 - Anastasia Pentina, Christoph H. Lampert:
Lifelong Learning with Non-i.i.d. Tasks. 1540-1548 - Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu:
Optimal Linear Estimation under Unknown Nonlinear Transform. 1549-1557 - Youssef Mroueh, Stephen Voinea, Tomaso A. Poggio:
Learning with Group Invariant Features: A Kernel Perspective. 1558-1566 - Xinyang Yi, Constantine Caramanis:
Regularized EM Algorithms: A Unified Framework and Statistical Guarantees. 1567-1575 - Soroosh Shafieezadeh-Abadeh, Peyman Mohajerin Esfahani, Daniel Kuhn:
Distributionally Robust Logistic Regression. 1576-1584 - Zhan Wei Lim, David Hsu, Wee Sun Lee:
Adaptive Stochastic Optimization: From Sets to Paths. 1585-1593 - Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz:
Beyond Convexity: Stochastic Quasi-Convex Optimization. 1594-1602 - Yuval Harel, Ron Meir, Manfred Opper:
A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding. 1603-1611 - Tengyu Ma, Avi Wigderson:
Sum-of-Squares Lower Bounds for Sparse PCA. 1612-1620 - Tian Tian, Jun Zhu:
Max-Margin Majority Voting for Learning from Crowds. 1621-1629 - Lorenzo Rosasco, Silvia Villa:
Learning with Incremental Iterative Regularization. 1630-1638 - Mahito Sugiyama, Karsten M. Borgwardt:
Halting in Random Walk Kernels. 1639-1647 - James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani:
MCMC for Variationally Sparse Gaussian Processes. 1648-1656 - Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco:
Less is More: Nyström Computational Regularization. 1657-1665 - Isabel Valera, Francisco J. R. Ruiz, Lennart Svensson, Fernando Pérez-Cruz:
Infinite Factorial Dynamical Model. 1666-1674 - Atsushi Shibagaki, Yoshiki Suzuki, Masayuki Karasuyama, Ichiro Takeuchi:
Regularization Path of Cross-Validation Error Lower Bounds. 1675-1683 - Dane S. Corneil, Wulfram Gerstner:
Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze-like Environments. 1684-1692 - Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom:
Teaching Machines to Read and Comprehend. 1693-1701 - Jonas Mueller, Tommi S. Jaakkola:
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions. 1702-1710 - Christopher R. Dance, Tomi Silander:
When are Kalman-Filter Restless Bandits Indexable? 1711-1719 - Ilya Shpitser:
Segregated Graphs and Marginals of Chain Graph Models. 1720-1728 - Mohammad Norouzi, Maxwell D. Collins, Matthew Johnson, David J. Fleet, Pushmeet Kohli:
Efficient Non-greedy Optimization of Decision Trees. 1729-1737 - Ye Wang, David B. Dunson:
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process. 1738-1746 - Quoc Phong Nguyen, Kian Hsiang Low, Patrick Jaillet:
Inverse Reinforcement Learning with Locally Consistent Reward Functions. 1747-1755 - Yossi Arjevani, Ohad Shamir:
Communication Complexity of Distributed Convex Learning and Optimization. 1756-1764 - Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He, Jianfeng Gao, Xinying Song, Li Deng:
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture. 1765-1773 - Chao Qian, Yang Yu, Zhi-Hua Zhou:
Subset Selection by Pareto Optimization. 1774-1782 - Jacob Andreas, Maxim Rabinovich, Michael I. Jordan, Dan Klein:
On the Accuracy of Self-Normalized Log-Linear Models. 1783-1791 - Junpei Komiyama, Junya Honda, Hiroshi Nakagawa:
Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring. 1792-1800 - Ariel D. Procaccia, Nisarg Shah:
Is Approval Voting Optimal Given Approval Votes? 1801-1809 - Michaël Perrot, Amaury Habrard:
Regressive Virtual Metric Learning. 1810-1818 - Huishuai Zhang, Yi Zhou, Yingbin Liang:
Analysis of Robust PCA via Local Incoherence. 1819-1827 - Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom:
Learning to Transduce with Unbounded Memory. 1828-1836 - Chongxuan Li, Jun Zhu, Tianlin Shi, Bo Zhang:
Max-Margin Deep Generative Models. 1837-1845 - Jeffrey Pennington, Felix X. Yu, Sanjiv Kumar:
Spherical Random Features for Polynomial Kernels. 1846-1854 - Djork-Arné Clevert, Andreas Mayr, Thomas Unterthiner, Sepp Hochreiter:
Rectified Factor Networks. 1855-1863 - Mauro Scanagatta, Cassio P. de Campos, Giorgio Corani, Marco Zaffalon:
Learning Bayesian Networks with Thousands of Variables. 1864-1872 - Ravi Sastry Ganti Mahapatruni, Laura Balzano, Rebecca Willett:
Matrix Completion Under Monotonic Single Index Models. 1873-1881 - Fereshteh Sadeghi, C. Lawrence Zitnick, Ali Farhadi:
Visalogy: Answering Visual Analogy Questions. 1882-1890 - Juho Lee, Seungjin Choi:
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models. 1891-1899 - Dan Alistarh, Jennifer Iglesias, Milan Vojnovic:
Streaming Min-max Hypergraph Partitioning. 1900-1908 - Sewoong Oh, Kiran Koshy Thekumparampil, Jiaming Xu:
Collaboratively Learning Preferences from Ordinal Data. 1909-1917 - Jonathan Vacher, Andrew Isaac Meso, Laurent U. Perrinet, Gabriel Peyré:
Biologically Inspired Dynamic Textures for Probing Motion Perception. 1918-1926 - Lucas Theis, Matthias Bethge:
Generative Image Modeling Using Spatial LSTMs. 1927-1935 - Wooseok Ha, Rina Foygel Barber:
Robust PCA with compressed data. 1936-1944 - Alkis Gotovos, S. Hamed Hassani, Andreas Krause:
Sampling from Probabilistic Submodular Models. 1945-1953 - Mehrdad Farajtabar, Yichen Wang, Manuel Gomez-Rodriguez, Shuang Li, Hongyuan Zha, Le Song:
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution. 1954-1962 - Ahmed Hefny, Carlton Downey, Geoffrey J. Gordon:
Supervised Learning for Dynamical System Learning. 1963-1971 - Noam Brown, Tuomas Sandholm:
Regret-Based Pruning in Extensive-Form Games. 1972-1980 - Kacper Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, Arthur Gretton:
Fast Two-Sample Testing with Analytic Representations of Probability Measures. 1981-1989 - Pedro H. O. Pinheiro, Ronan Collobert, Piotr Dollár:
Learning to Segment Object Candidates. 1990-1998 - Kyle R. Ulrich, David E. Carlson, Kafui Dzirasa, Lawrence Carin:
GP Kernels for Cross-Spectrum Analysis. 1999-2007 - Peter Kairouz, Sewoong Oh, Pramod Viswanath:
Secure Multi-party Differential Privacy. 2008-2016 - Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu:
Spatial Transformer Networks. 2017-2025 - Kevin Scaman, Rémi Lemonnier, Nicolas Vayatis:
Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks. 2026-2034 - Yunwen Lei, Ürün Dogan, Alexander Binder, Marius Kloft:
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms. 2035-2043 - Yuanjun Gao, Lars Buesing, Krishna V. Shenoy, John P. Cunningham:
High-dimensional neural spike train analysis with generalized count linear dynamical systems. 2044-2052 - Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya-Polo, Tomaso A. Poggio:
Learning with a Wasserstein Loss. 2053-2061 - Martin Slawski, Ping Li:
b-bit Marginal Regression. 2062-2070 - Guillaume Desjardins, Karen Simonyan, Razvan Pascanu, Koray Kavukcuoglu:
Natural Neural Networks. 2071-2079 - Edward Meeds, Max Welling:
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference. 2080-2088 - Tom Goldstein, Min Li, Xiaoming Yuan:
Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing. 2089-2097 - Pranjal Awasthi, Andrej Risteski:
On some provably correct cases of variational inference for topic models. 2098-2106 - Nikhil Rao, Hsiang-Fu Yu, Pradeep Ravikumar, Inderjit S. Dhillon:
Collaborative Filtering with Graph Information: Consistency and Scalable Methods. 2107-2115 - Richard Combes, Mohammad Sadegh Talebi, Alexandre Proutière, Marc Lelarge:
Combinatorial Bandits Revisited. 2116-2124 - Shakir Mohamed, Danilo Jimenez Rezende:
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning. 2125-2133 - Pinar Yanardag, S. V. N. Vishwanathan:
A Structural Smoothing Framework For Robust Graph Comparison. 2134-2142 - Alon Orlitsky, Ananda Theertha Suresh:
Competitive Distribution Estimation: Why is Good-Turing Good. 2143-2151 - Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama:
Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction. 2152-2160 - Maria Lomeli, Stefano Favaro, Yee Whye Teh:
A hybrid sampler for Poisson-Kingman mixture models. 2161-2169 - Xiao Li, Kannan Ramchandran:
An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching. 2170-2178 - Daniel Vainsencher, Han Liu, Tong Zhang:
Local Smoothness in Variance Reduced Optimization. 2179-2187 - Shafin Rahman, Neil D. B. Bruce:
Saliency, Scale and Information: Towards a Unifying Theory. 2188-2196 - Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari:
Fighting Bandits with a New Kind of Smoothness. 2197-2205 - Vidyashankar Sivakumar, Arindam Banerjee, Pradeep Ravikumar:
Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs. 2206-2214 - Rakesh Shivanna, Bibaswan K. Chatterjee, Raman Sankaran, Chiranjib Bhattacharyya, Francis R. Bach:
Spectral Norm Regularization of Orthonormal Representations for Graph Transduction. 2215-2223 - David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams:
Convolutional Networks on Graphs for Learning Molecular Fingerprints. 2224-2232 - Kai Wei, Rishabh K. Iyer, Shengjie Wang, Wenruo Bai, Jeff A. Bilmes:
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications. 2233-2241 - Jessa Bekker, Jesse Davis, Arthur Choi, Adnan Darwiche, Guy Van den Broeck:
Tractable Learning for Complex Probability Queries. 2242-2250 - Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen:
StopWasting My Gradients: Practical SVRG. 2251-2259 - Been Kim, Julie A. Shah, Finale Doshi-Velez:
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction. 2260-2268 - Cengiz Pehlevan, Dmitri B. Chklovskii:
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks. 2269-2277 - Changyou Chen, Nan Ding, Lawrence Carin:
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators. 2278-2286 - Siqi Sun, Mladen Kolar, Jinbo Xu:
Learning structured densities via infinite dimensional exponential families. 2287-2295 - Haoyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, Wei Xu:
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question. 2296-2304 - Thomas Hofmann, Aurélien Lucchi, Simon Lacoste-Julien, Brian McWilliams:
Variance Reduced Stochastic Gradient Descent with Neighbors. 2305-2313 - Yunpeng Pan, Evangelos A. Theodorou, Michail Kontitsis:
Sample Efficient Path Integral Control under Uncertainty. 2314-2322 - Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner:
Stochastic Expectation Propagation. 2323-2331 - Nicholas Ruozzi:
Exactness of Approximate MAP Inference in Continuous MRFs. 2332-2340 - Bo Xie, Yingyu Liang, Le Song:
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients. 2341-2349 - Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth:
Generalization in Adaptive Data Analysis and Holdout Reuse. 2350-2358 - Mithun Chakraborty, Sanmay Das:
Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents. 2359-2367 - Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon:
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent. 2368-2376 - Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber:
Training Very Deep Networks. 2377-2385 - Jacob R. Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham:
Bayesian Active Model Selection with an Application to Automated Audiometry. 2386-2394 - Nilesh Tripuraneni, Shixiang Gu, Hong Ge, Zoubin Ghahramani:
Particle Gibbs for Infinite Hidden Markov Models. 2395-2403 - Jean-Baptiste Schiratti, Stéphanie Allassonnière, Olivier Colliot, Stanley Durrleman:
Learning spatiotemporal trajectories from manifold-valued longitudinal data. 2404-2412 - Koosha Khalvati, Rajesh P. Rao:
A Bayesian Framework for Modeling Confidence in Perceptual Decision Making. 2413-2421 - Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro:
Path-SGD: Path-Normalized Optimization in Deep Neural Networks. 2422-2430 - Xiangyu Wang, Chenlei Leng, David B. Dunson:
On the consistency theory of high dimensional variable screening. 2431-2439 - Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus:
End-To-End Memory Networks. 2440-2448 - Oren Rippel, Jasper Snoek, Ryan P. Adams:
Spectral Representations for Convolutional Neural Networks. 2449-2457 - Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo:
Online Gradient Boosting. 2458-2466 - Zhe Gan, Chunyuan Li, Ricardo Henao, David E. Carlson, Lawrence Carin:
Deep Temporal Sigmoid Belief Networks for Sequence Modeling. 2467-2475 - Emile Richard, Georges Goetz, E. J. Chichilnisky:
Recognizing retinal ganglion cells in the dark. 2476-2484 - Michael Shvartsman, Vaibhav Srivastava, Jonathan D. Cohen:
A Theory of Decision Making Under Dynamic Context. 2485-2493 - Andrew C. Miller, Albert Wu, Jeffrey Regier, Jon McAuliffe, Dustin Lang, Prabhat, David Schlegel, Ryan P. Adams:
A Gaussian Process Model of Quasar Spectral Energy Distributions. 2494-2502 - D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison:
Hidden Technical Debt in Machine Learning Systems. 2503-2511 - Tian Gao, Qiang Ji:
Local Causal Discovery of Direct Causes and Effects. 2512-2520 - Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu:
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality. 2521-2529 - Mehryar Mohri, Andres Muñoz Medina:
Revenue Optimization against Strategic Buyers. 2530-2538 - Tejas D. Kulkarni, William F. Whitney, Pushmeet Kohli, Joshua B. Tenenbaum:
Deep Convolutional Inverse Graphics Network. 2539-2547 - Parikshit Shah, Nikhil Rao, Gongguo Tang:
Sparse and Low-Rank Tensor Decomposition. 2548-2556 - Wouter M. Koolen, Alan Malek, Peter L. Bartlett, Yasin Abbasi-Yadkori:
Minimax Time Series Prediction. 2557-2565 - Ilias Diakonikolas, Moritz Hardt, Ludwig Schmidt:
Differentially Private Learning of Structured Discrete Distributions. 2566-2574 - Diederik P. Kingma, Tim Salimans, Max Welling:
Variational Dropout and the Local Reparameterization Trick. 2575-2583 - Nakul Verma, Kristin Branson:
Sample Complexity of Learning Mahalanobis Distance Metrics. 2584-2592 - Jimmy Ba, Ruslan Salakhutdinov, Roger B. Grosse, Brendan J. Frey:
Learning Wake-Sleep Recurrent Attention Models. 2593-2601 - Eunho Yang, Aurélie C. Lozano:
Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso. 2602-2610 - Bhaswar B. Bhattacharya, Gregory Valiant:
Testing Closeness With Unequal Sized Samples. 2611-2619 - Wenye Li:
Estimating Jaccard Index with Missing Observations: A Matrix Calibration Approach. 2620-2628 - Shixiang Gu, Zoubin Ghahramani, Richard E. Turner:
Neural Adaptive Sequential Monte Carlo. 2629-2637 - Michalis K. Titsias, Miguel Lázaro-Gredilla:
Local Expectation Gradients for Black Box Variational Inference. 2638-2646 - Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. 2647-2655 - Kevin G. Jamieson, Lalit K. Jain, Chris Fernandez, Nicholas J. Glattard, Robert D. Nowak:
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning. 2656-2664 - Qingqing Huang, Sham M. Kakade:
Super-Resolution Off the Grid. 2665-2673 - Christopher De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré:
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms. 2674-2682 - Dan Rosenbaum, Yair Weiss:
The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors. 2683-2691 - Oriol Vinyals, Meire Fortunato, Navdeep Jaitly:
Pointer Networks. 2692-2700 - Arya Mazumdar, Ankit Singh Rawat:
Associative Memory via a Sparse Recovery Model. 2701-2709 - Moontae Lee, David Bindel, David M. Mimno:
Robust Spectral Inference for Joint Stochastic Matrix Factorization. 2710-2718 - Rasmus Kyng, Anup Rao, Sushant Sachdeva:
Fast, Provable Algorithms for Isotonic Regression in all L_p-norms. 2719-2727 - Hong Wang, Wei Xing, Kaiser Asif, Brian D. Ziebart:
Adversarial Prediction Games for Multivariate Losses. 2728-2736 - Xiangru Lian, Yijun Huang, Yuncheng Li, Ji Liu:
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization. 2737-2745 - Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, Martin A. Riedmiller:
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images. 2746-2754 - Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire:
Efficient and Parsimonious Agnostic Active Learning. 2755-2763 - Mathew Monfort, Brenden M. Lake, Brian D. Ziebart, Patrick Lucey, Joshua B. Tenenbaum:
Softstar: Heuristic-Guided Probabilistic Inference. 2764-2772 - Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey E. Hinton:
Grammar as a Foreign Language. 2773-2781 - Martin Slawski, Ping Li, Matthias Hein:
Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices. 2782-2790 - Alireza Makhzani, Brendan J. Frey:
Winner-Take-All Autoencoders. 2791-2799 - Ricardo Henao, Zhe Gan, James Lu, Lawrence Carin:
Deep Poisson Factor Modeling. 2800-2808 - Kenji Kawaguchi, Leslie Pack Kaelbling, Tomás Lozano-Pérez:
Bayesian Optimization with Exponential Convergence. 2809-2817 - Christoph Dann, Emma Brunskill:
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning. 2818-2826 - Jacob Steinhardt, Percy Liang:
Learning with Relaxed Supervision. 2827-2835 - Vitaly Feldman, Will Perkins, Santosh S. Vempala:
Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's. 2836-2844 - Walid Krichene, Alexandre M. Bayen, Peter L. Bartlett:
Accelerated Mirror Descent in Continuous and Discrete Time. 2845-2853 - Andrew Gordon Wilson, Christoph Dann, Christopher G. Lucas, Eric P. Xing:
The Human Kernel. 2854-2862 - Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, Satinder Singh:
Action-Conditional Video Prediction using Deep Networks in Atari Games. 2863-2871 - James R. Voss, Mikhail Belkin, Luis Rademacher:
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA. 2872-2880 - Baharan Mirzasoleiman, Amin Karbasi, Ashwinkumar Badanidiyuru, Andreas Krause:
Distributed Submodular Cover: Succinctly Summarizing Massive Data. 2881-2889 - Mark Kozdoba, Shie Mannor:
Community Detection via Measure Space Embedding. 2890-2898 - Gheorghe Comanici, Doina Precup, Prakash Panangaden:
Basis refinement strategies for linear value function approximation in MDPs. 2899-2907 - Sheng Chen, Arindam Banerjee:
Structured Estimation with Atomic Norms: General Bounds and Applications. 2908-2916 - Yi-An Ma, Tianqi Chen, Emily B. Fox:
A Complete Recipe for Stochastic Gradient MCMC. 2917-2925 - Ofer Dekel, Ronen Eldan, Tomer Koren:
Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff. 2926-2934 - Mark Herbster, Stephen Pasteris, Shaona Ghosh:
Online Prediction at the Limit of Zero Temperature. 2935-2943 - Nicolas Heess, Gregory Wayne, David Silver, Timothy P. Lillicrap, Tom Erez, Yuval Tassa:
Learning Continuous Control Policies by Stochastic Value Gradients. 2944-2952 - Mengye Ren, Ryan Kiros, Richard S. Zemel:
Exploring Models and Data for Image Question Answering. 2953-2961 - Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter:
Efficient and Robust Automated Machine Learning. 2962-2970 - David E. Carlson, Edo Collins, Ya-Ping Hsieh, Lawrence Carin, Volkan Cevher:
Preconditioned Spectral Descent for Deep Learning. 2971-2979 - Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C. Courville, Yoshua Bengio:
A Recurrent Latent Variable Model for Sequential Data. 2980-2988 - Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire:
Fast Convergence of Regularized Learning in Games. 2989-2997 - Marijn F. Stollenga, Wonmin Byeon, Marcus Liwicki, Jürgen Schmidhuber:
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation. 2998-3006 - Hadi Mohasel Afshar, Justin Domke:
Reflection, Refraction, and Hamiltonian Monte Carlo. 3007-3015 - Purnamrita Sarkar, Deepayan Chakrabarti, Peter J. Bickel:
The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels. 3016-3024 - Kunal Talwar, Abhradeep Thakurta, Li Zhang:
Nearly Optimal Private LASSO. 3025-3033 - Rafael M. Frongillo, Mark D. Reid:
Convergence Analysis of Prediction Markets via Randomized Subspace Descent. 3034-3042 - Mingyuan Zhou, Yulai Cong, Bo Chen:
The Poisson Gamma Belief Network. 3043-3051 - Murat A. Erdogdu, Andrea Montanari:
Convergence rates of sub-sampled Newton methods. 3052-3060 - Jason D. Hartline, Vasilis Syrgkanis, Éva Tardos:
No-Regret Learning in Bayesian Games. 3061-3069 - Roland Kwitt, Stefan Huber, Marc Niethammer, Weili Lin, Ulrich Bauer:
Statistical Topological Data Analysis - A Kernel Perspective. 3070-3078 - Andrew M. Dai, Quoc V. Le:
Semi-supervised Sequence Learning. 3079-3087 - Vikas Sindhwani, Tara N. Sainath, Sanjiv Kumar:
Structured Transforms for Small-Footprint Deep Learning. 3088-3096 - Christopher De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré:
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width. 3097-3105 - Qinqing Zheng, Ryota Tomioka:
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm. 3106-3113 - Marin Kobilarov:
Sample Complexity Bounds for Iterative Stochastic Policy Optimization. 3114-3122 - Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David:
BinaryConnect: Training Deep Neural Networks with binary weights during propagations. 3123-3131 - Igor Mordatch, Kendall Lowrey, Galen Andrew, Zoran Popovic, Emanuel Todorov:
Interactive Control of Diverse Complex Characters with Neural Networks. 3132-3140 - Jennifer Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes:
Submodular Hamming Metrics. 3141-3149 - Alp Yurtsever, Quoc Tran-Dinh, Volkan Cevher:
A Universal Primal-Dual Convex Optimization Framework. 3150-3158 - Özgür Simsek, Marcus Buckmann:
Learning From Small Samples: An Analysis of Simple Decision Heuristics. 3159-3167 - Gergely Neu:
Explore no more: Improved high-probability regret bounds for non-stochastic bandits. 3168-3176 - Se-Young Yun, Marc Lelarge, Alexandre Proutière:
Fast and Memory Optimal Low-Rank Matrix Approximation. 3177-3185 - Harikrishna Narasimhan, David C. Parkes, Yaron Singer:
Learnability of Influence in Networks. 3186-3194 - Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath:
Learning Causal Graphs with Small Interventions. 3195-3203 - Yaron Singer, Jan Vondrák:
Information-theoretic lower bounds for convex optimization with erroneous oracles. 3204-3212 - David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon:
Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial. 3213-3221 - Piyush Rai, Changwei Hu, Ricardo Henao, Lawrence Carin:
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings. 3222-3230 - Adith Swaminathan, Thorsten Joachims:
The Self-Normalized Estimator for Counterfactual Learning. 3231-3239 - Somdeb Sarkhel, Parag Singla, Vibhav Gogate:
Fast Lifted MAP Inference via Partitioning. 3240-3248 - Philip Bachman, Doina Precup:
Data Generation as Sequential Decision Making. 3249-3257 - Rafael M. Frongillo, Ian A. Kash:
On Elicitation Complexity. 3258-3266 - Wei Ping, Qiang Liu, Alexander Ihler:
Decomposition Bounds for Marginal MAP. 3267-3275 - Meisam Razaviyayn, Farzan Farnia, David Tse:
Discrete Rényi Classifiers. 3276-3284 - Yali Wan, Marina Meila:
A class of network models recoverable by spectral clustering. 3285-3293 - Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Raquel Urtasun, Antonio Torralba, Sanja Fidler:
Skip-Thought Vectors. 3294-3302 - Sergey M. Plis, David Danks, Cynthia Freeman, Vince D. Calhoun:
Rate-Agnostic (Causal) Structure Learning. 3303-3311 - Vivien Seguy, Marco Cuturi:
Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric. 3312-3320 - Oluwasanmi Koyejo, Nagarajan Natarajan, Pradeep Ravikumar, Inderjit S. Dhillon:
Consistent Multilabel Classification. 3321-3329 - Amar Shah, Zoubin Ghahramani:
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions. 3330-3338 - Julien Audiffren, Liva Ralaivola:
Cornering Stationary and Restless Mixing Bandits with Remix-UCB. 3339-3347 - Danilo Bzdok, Michael Eickenberg, Olivier Grisel, Bertrand Thirion, Gaël Varoquaux:
Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data. 3348-3356 - David A. Moore, Stuart J. Russell:
Gaussian Process Random Fields. 3357-3365 - Shuang Li, Yao Xie, Hanjun Dai, Le Song:
M-Statistic for Kernel Change-Point Detection. 3366-3374 - Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan:
Adaptive Online Learning. 3375-3383 - Hongzhou Lin, Julien Mairal, Zaïd Harchaoui:
A Universal Catalyst for First-Order Optimization. 3384-3392 - Rémi Bardenet, Michalis K. Titsias:
Inference for determinantal point processes without spectral knowledge. 3393-3401 - Mohammad E. Khan, Pierre Baqué, François Fleuret, Pascal Fua:
Kullback-Leibler Proximal Variational Inference. 3402-3410 - Niao He, Zaïd Harchaoui:
Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization. 3411-3419 - Christos Thrampoulidis, Ehsan Abbasi, Babak Hassibi:
LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements. 3420-3428 - Tatsunori B. Hashimoto, Yi Sun, Tommi S. Jaakkola:
From random walks to distances on unweighted graphs. 3429-3437 - Anoop Korattikara Balan, Vivek Rathod, Kevin P. Murphy, Max Welling:
Bayesian dark knowledge. 3438-3446 - Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon:
Matrix Completion with Noisy Side Information. 3447-3455 - Scott W. Linderman, Matthew J. Johnson, Ryan P. Adams:
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation. 3456-3464 - Keenon Werling, Arun Tejasvi Chaganty, Percy Liang, Christopher D. Manning:
On-the-Job Learning with Bayesian Decision Theory. 3465-3473 - Volodymyr Kuleshov, Percy Liang:
Calibrated Structured Prediction. 3474-3482 - Kihyuk Sohn, Honglak Lee, Xinchen Yan:
Learning Structured Output Representation using Deep Conditional Generative Models. 3483-3491 - Nan Du, Yichen Wang, Niao He, Jimeng Sun, Le Song:
Time-Sensitive Recommendation From Recurrent User Activities. 3492-3500 - Felipe A. Tobar, Thang D. Bui, Richard E. Turner:
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels. 3501-3509 - Bo Waggoner, Rafael M. Frongillo, Jacob D. Abernethy:
A Market Framework for Eliciting Private Data. 3510-3518 - Happy Mittal, Anuj Mahajan, Vibhav Gogate, Parag Singla:
Lifted Inference Rules With Constraints. 3519-3527 - John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel:
Gradient Estimation Using Stochastic Computation Graphs. 3528-3536 - Abbas Abdolmaleki, Rudolf Lioutikov, Jan Peters, Nuno Lau, Luís Paulo Reis, Gerhard Neumann:
Model-Based Relative Entropy Stochastic Search. 3537-3545 - Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, Tapani Raiko:
Semi-supervised Learning with Ladder Networks. 3546-3554 - Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Nan Ding, Dale Schuurmans:
Embedding Inference for Structured Multilabel Prediction. 3555-3563 - Dustin Tran, David M. Blei, Edoardo M. Airoldi:
Copula variational inference. 3564-3572 - Kisuk Lee, Aleksandar Zlateski, Ashwin Vishwanathan, H. Sebastian Seung:
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction. 3573-3581 - Ian En-Hsu Yen, Shan-Wei Lin, Shou-De Lin:
A Dual Augmented Block Minimization Framework for Learning with Limited Memory. 3582-3590 - Jayadev Acharya, Constantinos Daskalakis, Gautam Kamath:
Optimal Testing for Properties of Distributions. 3591-3599 - Yu-Ying Liu, Shuang Li, Fuxin Li, Le Song, James M. Rehg:
Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression. 3600-3608 - Thibaut Liénart, Yee Whye Teh, Arnaud Doucet:
Expectation Particle Belief Propagation. 3609-3617 - Mingjun Zhong, Nigel H. Goddard, Charles Sutton:
Latent Bayesian melding for integrating individual and population models. 3618-3626
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