Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID26. NIPS 2013: Lake Tahoe, Nevada, United States
- Christopher J. C. Burges, Léon Bottou, Zoubin Ghahramani, Kilian Q. Weinberger:
Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States. 2013
- David López-Paz, Philipp Hennig, Bernhard Schölkopf:
The Randomized Dependence Coefficient. 1-9 - Alessandro Perina, Nebojsa Jojic, Manuele Bicego, Andrzej Truski:
Documents as multiple overlapping windows into grids of counts. 10-18 - Wenhao Zhang, Si Wu:
Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively. 19-27 - Guang-Tong Zhou, Tian Lan, Arash Vahdat, Greg Mori:
Latent Maximum Margin Clustering. 28-36 - Martin Mevissen, Emanuele Ragnoli, Jia Yuan Yu:
Data-driven Distributionally Robust Polynomial Optimization. 37-45 - Marcus Rohrbach, Sandra Ebert, Bernt Schiele:
Transfer Learning in a Transductive Setting. 46-54 - Ali Borji, Laurent Itti:
Bayesian optimization explains human active search. 55-63 - Yu-Xiang Wang, Huan Xu, Chenlei Leng:
Provable Subspace Clustering: When LRR meets SSC. 64-72 - Hossein Azari Soufiani, Hansheng Diao, Zhenyu Lai, David C. Parkes:
Generalized Random Utility Models with Multiple Types. 73-81 - Xinhua Zhang, Yaoliang Yu, Dale Schuurmans:
Polar Operators for Structured Sparse Estimation. 82-90 - Yaoliang Yu:
On Decomposing the Proximal Map. 91-99 - Liam MacDermed, Charles L. Isbell Jr.:
Point Based Value Iteration with Optimal Belief Compression for Dec-POMDPs. 100-108 - Ilya O. Tolstikhin, Yevgeny Seldin:
PAC-Bayes-Empirical-Bernstein Inequality. 109-117 - Chen-Ping Yu, Wen-Yu Hua, Dimitris Samaras, Gregory J. Zelinsky:
Modeling Clutter Perception using Parametric Proto-object Partitioning. 118-126 - Marcelo Fiori, Pablo Sprechmann, Joshua T. Vogelstein, Pablo Musé, Guillermo Sapiro:
Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching. 127-135 - Elias Bareinboim, Sanghack Lee, Vasant G. Honavar, Judea Pearl:
Transportability from Multiple Environments with Limited Experiments. 136-144 - Amit Daniely, Nati Linial, Shai Shalev-Shwartz:
More data speeds up training time in learning halfspaces over sparse vectors. 145-153 - Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Causal Inference on Time Series using Restricted Structural Equation Models. 154-162 - Karen Simonyan, Andrea Vedaldi, Andrew Zisserman:
Deep Fisher Networks for Large-Scale Image Classification. 163-171 - Lei Shi:
Sparse Additive Text Models with Low Rank Background. 172-180 - Chong Wang, Xi Chen, Alexander J. Smola, Eric P. Xing:
Variance Reduction for Stochastic Gradient Optimization. 181-189 - Michiel Hermans, Benjamin Schrauwen:
Training and Analysing Deep Recurrent Neural Networks. 190-198 - Jeffrey W. Miller, Matthew T. Harrison:
A simple example of Dirichlet process mixture inconsistency for the number of components. 199-206 - Sergey Levine, Vladlen Koltun:
Variational Policy Search via Trajectory Optimization. 207-215 - Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten M. Borgwardt:
Scalable kernels for graphs with continuous attributes. 216-224 - Ulrike von Luxburg, Morteza Alamgir:
Density estimation from unweighted k-nearest neighbor graphs: a roadmap. 225-233 - Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John M. Winn, Antonio Criminisi:
Decision Jungles: Compact and Rich Models for Classification. 234-242 - Zhenwen Dai, Georgios Exarchakis, Jörg Lücke:
What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach. 243-251 - Prashanth L. A., Mohammad Ghavamzadeh:
Actor-Critic Algorithms for Risk-Sensitive MDPs. 252-260 - Isik Baris Fidaner, Ali Taylan Cemgil:
Summary Statistics for Partitionings and Feature Allocations. 261-269 - Lee H. Dicker, Dean P. Foster:
One-shot learning and big data with n=2. 270-278 - Michalis K. Titsias, Miguel Lázaro-Gredilla:
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression. 279-287 - Cristina Savin, Peter Dayan, Máté Lengyel:
Correlations strike back (again): the case of associative memory retrieval. 288-296 - Zhuo Wang, Alan A. Stocker, Daniel D. Lee:
Optimal Neural Population Codes for High-dimensional Stimulus Variables. 297-305 - Ryan D. Turner, Steven Bottone, Clay J. Stanek:
Online Variational Approximations to non-Exponential Family Change Point Models: With Application to Radar Tracking. 306-314 - Rie Johnson, Tong Zhang:
Accelerating Stochastic Gradient Descent using Predictive Variance Reduction. 315-323 - Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana:
Using multiple samples to learn mixture models. 324-332 - Tzu-Kuo Huang, Jeff G. Schneider:
Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition. 333-341 - Jason D. Lee, Yuekai Sun, Jonathan E. Taylor:
On model selection consistency of penalized M-estimators: a geometric theory. 342-350 - Stefan Wager, Sida Wang, Percy Liang:
Dropout Training as Adaptive Regularization. 351-359 - Paramveer S. Dhillon, Yichao Lu, Dean P. Foster, Lyle H. Ungar:
New Subsampling Algorithms for Fast Least Squares Regression. 360-368 - Yichao Lu, Paramveer S. Dhillon, Dean P. Foster, Lyle H. Ungar:
Faster Ridge Regression via the Subsampled Randomized Hadamard Transform. 369-377 - Shai Shalev-Shwartz, Tong Zhang:
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent. 378-385 - Bruno Scherrer:
Improved and Generalized Upper Bounds on the Complexity of Policy Iteration. 386-394 - Dahua Lin:
Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation. 395-403 - Jiashi Feng, Huan Xu, Shuicheng Yan:
Online Robust PCA via Stochastic Optimization. 404-412 - Fabian H. Sinz, Anna Stockl, Jan Grewe, Jan Benda:
Least Informative Dimensions. 413-421 - Junming Yin, Qirong Ho, Eric P. Xing:
A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks. 422-430 - Gilles Louppe, Louis Wehenkel, Antonio Sutera, Pierre Geurts:
Understanding variable importances in forests of randomized trees. 431-439 - Brian McWilliams, David Balduzzi, Joachim M. Buhmann:
Correlated random features for fast semi-supervised learning. 440-448 - Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin:
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture. 449-457 - Yaoliang Yu:
Better Approximation and Faster Algorithm Using the Proximal Average. 458-466 - Mahito Sugiyama, Karsten M. Borgwardt:
Rapid Distance-Based Outlier Detection via Sampling. 467-475 - Po-Ling Loh, Martin J. Wainwright:
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima. 476-484 - Carlos J. Becker, C. Mario Christoudias, Pascal Fua:
Non-Linear Domain Adaptation with Boosting. 485-493 - Carl Doersch, Abhinav Gupta, Alexei A. Efros:
Mid-level Visual Element Discovery as Discriminative Mode Seeking. 494-502 - Assaf Glazer, Michael Lindenbaum, Shaul Markovitch:
q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions. 503-511 - Sivan Sabato, Anand D. Sarwate, Nati Srebro:
Auditing: Active Learning with Outcome-Dependent Query Costs. 512-520 - José Bento, Nate Derbinsky, Javier Alonso-Mora, Jonathan S. Yedidia:
A message-passing algorithm for multi-agent trajectory planning. 521-529 - Yichuan Tang, Ruslan Salakhutdinov:
Learning Stochastic Feedforward Neural Networks. 530-538 - Srinivas C. Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Häusser, Jakob H. Macke:
Inferring neural population dynamics from multiple partial recordings of the same neural circuit. 539-547 - Ian J. Goodfellow, Mehdi Mirza, Aaron C. Courville, Yoshua Bengio:
Multi-Prediction Deep Boltzmann Machines. 548-556 - Vibhav Vineet, Carsten Rother, Philip H. S. Torr:
Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation. 557-565 - Christophe Schülke, Francesco Caltagirone, Florent Krzakala, Lenka Zdeborová:
Blind Calibration in Compressed Sensing using Message Passing Algorithms. 566-574 - Ashesh Jain, Brian Wojcik, Thorsten Joachims, Ashutosh Saxena:
Learning Trajectory Preferences for Manipulators via Iterative Improvement. 575-583 - Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon:
Large Scale Distributed Sparse Precision Estimation. 584-592 - Cheston Tan, Jedediah M. Singer, Thomas Serre, David Sheinberg, Tomaso A. Poggio:
Neural representation of action sequences: how far can a simple snippet-matching model take us? 593-601 - Siwei Lyu, Xin Wang:
On Algorithms for Sparse Multi-factor NMF. 602-610 - Eunho Yang, Pradeep Ravikumar:
Dirty Statistical Models. 611-619 - Jason Chang, John W. Fisher III:
Parallel Sampling of DP Mixture Models using Sub-Cluster Splits. 620-628 - Tianbao Yang:
Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent. 629-637 - Sébastien Bubeck, Che-Yu Liu:
Prior-free and prior-dependent regret bounds for Thompson Sampling. 638-646 - Justin Domke:
Structured Learning via Logistic Regression. 647-655 - Parikshit Ram, Alexander G. Gray:
Which Space Partitioning Tree to Use for Search? 656-664 - Justin Domke, Xianghang Liu:
Projecting Ising Model Parameters for Fast Mixing. 665-673 - Mehrdad Mahdavi, Lijun Zhang, Rong Jin:
Mixed Optimization for Smooth Functions. 674-682 - Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu:
Conditional Random Fields via Univariate Exponential Families. 683-691 - Edoardo M. Airoldi, Thiago B. Costa, Stanley H. Chan:
Stochastic blockmodel approximation of a graphon: Theory and consistent estimation. 692-700 - Shiau Hong Lim, Huan Xu, Shie Mannor:
Reinforcement Learning in Robust Markov Decision Processes. 701-709 - Ke Hou, Zirui Zhou, Anthony Man-Cho So, Zhi-Quan Luo:
On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization. 710-718 - Hesham Mostafa, Lorenz K. Müller, Giacomo Indiveri:
Recurrent networks of coupled Winner-Take-All oscillators for solving constraint satisfaction problems. 719-727 - Wenjie Luo, Alexander G. Schwing, Raquel Urtasun:
Latent Structured Active Learning. 728-736 - Nicolò Cesa-Bianchi, Claudio Gentile, Giovanni Zappella:
A Gang of Bandits. 737-745 - Daniel Hernández-Lobato, José Miguel Hernández-Lobato:
Learning Feature Selection Dependencies in Multi-task Learning. 746-754 - Wojciech Zaremba, Arthur Gretton, Matthew B. Blaschko:
B-test: A Non-parametric, Low Variance Kernel Two-sample Test. 755-763 - Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan:
Online PCA for Contaminated Data. 764-772 - Francis R. Bach, Eric Moulines:
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n). 773-781 - Ziteng Wang, Kai Fan, Jiaqi Zhang, Liwei Wang:
Efficient Algorithm for Privately Releasing Smooth Queries. 782-790 - Anshumali Shrivastava, Ping Li:
Beyond Pairwise: Provably Fast Algorithms for Approximate k-Way Similarity Search. 791-799 - Raphaël Bailly, Xavier Carreras, Ariadna Quattoni:
Unsupervised Spectral Learning of Finite State Transducers. 800-808 - Naiyan Wang, Dit-Yan Yeung:
Learning a Deep Compact Image Representation for Visual Tracking. 809-817 - Ferran Diego Andilla, Fred A. Hamprecht:
Learning Multi-level Sparse Representations. 818-826 - Grani Adiwena Hanasusanto, Daniel Kuhn:
Robust Data-Driven Dynamic Programming. 827-835 - Akshay Krishnamurthy, Aarti Singh:
Low-Rank Matrix and Tensor Completion via Adaptive Sampling. 836-844 - Adrien Todeschini, François Caron, Marie Chavent:
Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms. 845-853 - Eshcar Hillel, Zohar Shay Karnin, Tomer Koren, Ronny Lempel, Oren Somekh:
Distributed Exploration in Multi-Armed Bandits. 854-862 - Wouter M. Koolen:
The Pareto Regret Frontier. 863-871 - Shaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang:
Direct 0-1 Loss Minimization and Margin Maximization with Boosting. 872-880 - Asrar Ahmed, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet:
Regret based Robust Solutions for Uncertain Markov Decision Processes. 881-889 - Chris Hinrichs, Vamsi K. Ithapu, Qinyuan Sun, Sterling C. Johnson, Vikas Singh:
Speeding up Permutation Testing in Neuroimaging. 890-898 - Yoshua Bengio, Li Yao, Guillaume Alain, Pascal Vincent:
Generalized Denoising Auto-Encoders as Generative Models. 899-907 - Pablo Sprechmann, Roee Litman, Tal Ben Yakar, Alexander M. Bronstein, Guillermo Sapiro:
Supervised Sparse Analysis and Synthesis Operators. 908-916 - Ryosuke Matsushita, Toshiyuki Tanaka:
Low-rank matrix reconstruction and clustering via approximate message passing. 917-925 - Richard Socher, Danqi Chen, Christopher D. Manning, Andrew Y. Ng:
Reasoning With Neural Tensor Networks for Knowledge Base Completion. 926-934 - Richard Socher, Milind Ganjoo, Christopher D. Manning, Andrew Y. Ng:
Zero-Shot Learning Through Cross-Modal Transfer. 935-943 - Mohsen Bayati, Murat A. Erdogdu, Andrea Montanari:
Estimating LASSO Risk and Noise Level. 944-952 - David J. Weiss, Ben Taskar:
Learning Adaptive Value of Information for Structured Prediction. 953-961 - Dae Il Kim, Prem Gopalan, David M. Blei, Erik B. Sudderth:
Efficient Online Inference for Bayesian Nonparametric Relational Models. 962-970 - Botond Cseke, Manfred Opper, Guido Sanguinetti:
Approximate inference in latent Gaussian-Markov models from continuous time observations. 971-979 - Lijun Zhang, Mehrdad Mahdavi, Rong Jin:
Linear Convergence with Condition Number Independent Access of Full Gradients. 980-988 - Divyanshu Vats, Richard G. Baraniuk:
When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements. 989-997 - Raif M. Rustamov, Leonidas J. Guibas:
Wavelets on Graphs via Deep Learning. 998-1006 - Wojciech Samek, Duncan A. J. Blythe, Klaus-Robert Müller, Motoaki Kawanabe:
Robust Spatial Filtering with Beta Divergence. 1007-1015 - Fajwel Fogel, Rodolphe Jenatton, Francis R. Bach, Alexandre d'Aspremont:
Convex Relaxations for Permutation Problems. 1016-1024 - Josip Djolonga, Andreas Krause, Volkan Cevher:
High-Dimensional Gaussian Process Bandits. 1025-1033 - Subhaneil Lahiri, Surya Ganguli:
A memory frontier for complex synapses. 1034-1042 - Tim Roughgarden, Michael J. Kearns:
Marginals-to-Models Reducibility. 1043-1051 - Nima Taghipour, Jesse Davis, Hendrik Blockeel:
First-order Decomposition Trees. 1052-1060 - Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jordan:
A Comparative Framework for Preconditioned Lasso Algorithms. 1061-1069 - Jie Wang, Jiayu Zhou, Peter Wonka, Jieping Ye:
Lasso Screening Rules via Dual Polytope Projection. 1070-1078 - Yuening Hu, Jordan L. Boyd-Graber, Hal Daumé III, Z. Irene Ying:
Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent. 1079-1087 - George H. Chen, Stanislav Nikolov, Devavrat Shah:
A Latent Source Model for Nonparametric Time Series Classification. 1088-1096 - Yanshuai Cao, Marcus A. Brubaker, David J. Fleet, Aaron Hertzmann:
Efficient Optimization for Sparse Gaussian Process Regression. 1097-1105