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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