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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 L. 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 - Viet-An Nguyen, Jordan L. Boyd-Graber, Philip Resnik:
Lexical and Hierarchical Topic Regression. 1106-1114 - Mehrdad Mahdavi, Tianbao Yang, Rong Jin:
Stochastic Convex Optimization with Multiple Objectives. 1115-1123 - Dino Sejdinovic, Arthur Gretton, Wicher Bergsma:
A Kernel Test for Three-Variable Interactions. 1124-1132 - Michael C. Hughes, Erik B. Sudderth:
Memoized Online Variational Inference for Dirichlet Process Mixture Models. 1133-1141 - Liming Wang, David E. Carlson, Miguel R. D. Rodrigues, David Wilcox, A. Robert Calderbank, Lawrence Carin:
Designed Measurements for Vector Count Data. 1142-1150 - Yuhong Guo:
Robust Transfer Principal Component Analysis with Rank Constraints. 1151-1159 - Nicolò Cesa-Bianchi, Ofer Dekel, Ohad Shamir:
Online Learning with Switching Costs and Other Adaptive Adversaries. 1160-1168 - Kareem Amin, Afshin Rostamizadeh, Umar Syed:
Learning Prices for Repeated Auctions with Strategic Buyers. 1169-1177 - Miaomiao Zhang, P. Thomas Fletcher:
Probabilistic Principal Geodesic Analysis. 1178-1186 - Adel Javanmard, Andrea Montanari:
Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models. 1187-1195 - Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari:
Learning with Noisy Labels. 1196-1204 - Erich Kummerfeld, David Danks:
Tracking Time-varying Graphical Structure. 1205-1213 - Kohei Hayashi, Ryohei Fujimaki:
Factorized Asymptotic Bayesian Inference for Latent Feature Models. 1214-1222 - Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B. Gibbons, Garth A. Gibson, Gregory R. Ganger, Eric P. Xing:
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server. 1223-1231 - Jie Liu, David Page:
Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models. 1232-1240 - Navid Zolghadr, Gábor Bartók, Russell Greiner, András György, Csaba Szepesvári:
Online Learning with Costly Features and Labels. 1241-1249 - Eftychios A. Pnevmatikakis, Liam Paninski:
Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions. 1250-1258 - Min Xiao, Yuhong Guo:
A Novel Two-Step Method for Cross Language Representation Learning. 1259-1267 - Tamir Hazan, Subhransu Maji, Tommi S. Jaakkola:
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations. 1268-1276 - Karthika Mohan, Judea Pearl, Jin Tian:
Graphical Models for Inference with Missing Data. 1277-1285 - Boqing Gong, Kristen Grauman, Fei Sha:
Reshaping Visual Datasets for Domain Adaptation. 1286-1294 - Maria-Florina Balcan, Vitaly Feldman:
Statistical Active Learning Algorithms. 1295-1303 - Benjamin Shababo, Brooks Paige, Ari Pakman, Liam Paninski:
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits. 1304-1312 - Stefanie Jegelka, Francis R. Bach, Suvrit Sra:
Reflection methods for user-friendly submodular optimization. 1313-1321 - Kewei Tu, Maria Pavlovskaia, Song Chun Zhu:
Unsupervised Structure Learning of Stochastic And-Or Grammars. 1322-1330 - Ryota Tomioka, Taiji Suzuki:
Convex Tensor Decomposition via Structured Schatten Norm Regularization. 1331-1339 - Yann N. Dauphin, Yoshua Bengio:
Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs. 1340-1348 - Jukka Corander, Tomi Janhunen, Jussi Rintanen, Henrik J. Nyman, Johan Pensar:
Learning Chordal Markov Networks by Constraint Satisfaction. 1349-1357 - Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima:
Parametric Task Learning. 1358-1366 - Zhengdong Lu, Hang Li:
A Deep Architecture for Matching Short Texts. 1367-1375 - Christina E. Lee, Asuman E. Ozdaglar, Devavrat Shah:
Computing the Stationary Distribution Locally. 1376-1384 - Myunghwan Kim, Jure Leskovec:
Nonparametric Multi-group Membership Model for Dynamic Networks. 1385-1393 - Matteo Pirotta, Marcello Restelli, Luca Bascetta:
Adaptive Step-Size for Policy Gradient Methods. 1394-1402 - Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan:
Optimistic Concurrency Control for Distributed Unsupervised Learning. 1403-1411 - Leonidas Lefakis, François Fleuret:
Reservoir Boosting : Between Online and Offline Ensemble Learning. 1412-1420 - Xavier Bresson, Thomas Laurent, David Uminsky, James H. von Brecht:
Multiclass Total Variation Clustering. 1421-1429 - Raja Hafiz Affandi, Emily B. Fox, Ben Taskar:
Approximate Inference in Continuous Determinantal Processes. 1430-1438 - Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi:
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering. 1439-1447 - Nathaniel Korda, Emilie Kaufmann, Rémi Munos:
Thompson Sampling for 1-Dimensional Exponential Family Bandits. 1448-1456 - Viet Cuong Nguyen, Wee Sun Lee, Nan Ye, Kian Ming Adam Chai, Hai Leong Chieu:
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion. 1457-1465 - Barbara Rakitsch, Christoph Lippert, Karsten M. Borgwardt, Oliver Stegle:
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals. 1466-1474 - Harish G. Ramaswamy, Shivani Agarwal, Ambuj Tewari:
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses. 1475-1483 - Qichao Que, Mikhail Belkin:
Inverse Density as an Inverse Problem: the Fredholm Equation Approach. 1484-1492 - Forest Agostinelli, Michael R. Anderson, Honglak Lee:
Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising. 1493-1501 - Khaled S. Refaat, Arthur Choi, Adnan Darwiche:
EDML for Learning Parameters in Directed and Undirected Graphical Models. 1502-1510 - Soravit Changpinyo, Kuan Liu, Fei Sha:
Similarity Component Analysis. 1511-1519 - Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Joshua B. Tenenbaum:
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs. 1520-1528 - John C. Duchi, Martin J. Wainwright, Michael I. Jordan:
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation. 1529-1537 - David G. T. Barrett, Sophie Denève, Christian K. Machens:
Firing rate predictions in optimal balanced networks. 1538-1546 - Masayuki Karasuyama, Hiroshi Mamitsuka:
Manifold-based Similarity Adaptation for Label Propagation. 1547-1555 - Haichao Zhang, David P. Wipf:
Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty. 1556-1564 - Dimitris Achlioptas, Zohar Shay Karnin, Edo Liberty:
Near-Optimal Entrywise Sampling for Data Matrices. 1565-1573 - Leonid Boytsov, Bilegsaikhan Naidan:
Learning to Prune in Metric and Non-Metric Spaces. 1574-1582 - Alexander Zimin, Gergely Neu:
Online learning in episodic Markovian decision processes by relative entropy policy search. 1583-1591 - Paul Wagner:
Optimistic policy iteration and natural actor-critic: A unifying view and a non-optimality result. 1592-1600 - Charles Blundell, Yee Whye Teh:
Bayesian Hierarchical Community Discovery. 1601-1609 - Noga Alon, Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour:
From Bandits to Experts: A Tale of Domination and Independence. 1610-1618 - Cosma Rohilla Shalizi, Aryeh Kontorovich:
Predictive PAC Learning and Process Decompositions. 1619-1627 - Crystal Maung, Haim Schweitzer:
Pass-efficient unsupervised feature selection. 1628-1636 - Xiaoqin Zhang, Di Wang, Zhengyuan Zhou, Yi Ma:
Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors. 1637-1645 - Aijun Bai, Feng Wu, Xiaoping Chen:
Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search. 1646-1654 - Tetsuro Morimura, Takayuki Osogami, Tsuyoshi Idé:
Solving inverse problem of Markov chain with partial observations. 1655-1663 - Daniele Durante, Bruno Scarpa, David B. Dunson:
Locally Adaptive Bayesian Multivariate Time Series. 1664-1672 - Yannick Schwartz, Bertrand Thirion, Gaël Varoquaux:
Mapping paradigm ontologies to and from the brain. 1673-1681 - Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi, Lav R. Varshney:
Noise-Enhanced Associative Memories. 1682-1690 - Shouyuan Chen, Michael R. Lyu, Irwin King, Zenglin Xu:
Exact and Stable Recovery of Pairwise Interaction Tensors. 1691-1699 - Evan Archer, Il Memming Park, Jonathan W. Pillow:
Bayesian entropy estimation for binary spike train data using parametric prior knowledge. 1700-1708 - Christian Albers, Maren Westkott, Klaus Pawelzik:
Perfect Associative Learning with Spike-Timing-Dependent Plasticity. 1709-1717 - Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu:
On Poisson Graphical Models. 1718-1726 - Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan:
Streaming Variational Bayes. 1727-1735 - José Miguel Hernández-Lobato, James Robert Lloyd, Daniel Hernández-Lobato:
Gaussian Process Conditional Copulas with Applications to Financial Time Series. 1736-1744 - Marius Pachitariu, Adam M. Packer, Noah Pettit, Henry Dalgleish, Michael Häusser, Maneesh Sahani:
Extracting regions of interest from biological images with convolutional sparse block coding. 1745-1753 - Victor Gabillon, Mohammad Ghavamzadeh, Bruno Scherrer:
Approximate Dynamic Programming Finally Performs Well in the Game of Tetris. 1754-1762 - Matthew Lawlor, Steven W. Zucker:
Third-Order Edge Statistics: Contour Continuation, Curvature, and Cortical Connections. 1763-1771 - Adhiraj Somani, Nan Ye, David Hsu, Wee Sun Lee:
DESPOT: Online POMDP Planning with Regularization. 1772-1780 - Troy Lee, Adi Shraibman:
Matrix Completion From any Given Set of Observations. 1781-1787 - Samory Kpotufe, Francesco Orabona:
Regression-tree Tuning in a Streaming Setting. 1788-1796 - Francesca Petralia, Joshua T. Vogelstein, David B. Dunson:
Multiscale Dictionary Learning for Estimating Conditional Distributions. 1797-1805 - Francesco Orabona:
Dimension-Free Exponentiated Gradient. 1806-1814 - Raman Arora, Andrew Cotter, Nati Srebro:
Stochastic Optimization of PCA with Capped MSG. 1815-1823 - Rohit Babbar, Ioannis Partalas, Éric Gaussier, Massih-Reza Amini:
On Flat versus Hierarchical Classification in Large-Scale Taxonomies. 1824-1832 - Ying Liu, Alan S. Willsky:
Learning Gaussian Graphical Models with Observed or Latent FVSs. 1833-1841 - Yangqing Jia, Joshua T. Abbott, Joseph L. Austerweil, Thomas L. Griffiths, Trevor Darrell:
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies. 1842-1850 - Moustapha Cissé, Nicolas Usunier, Thierry Artières, Patrick Gallinari:
Robust Bloom Filters for Large MultiLabel Classification Tasks. 1851-1859 - Deepti Pachauri, Risi Kondor, Vikas Singh:
Solving the multi-way matching problem by permutation synchronization. 1860-1868 - Daniel Bartz, Klaus-Robert Müller:
Generalizing Analytic Shrinkage for Arbitrary Covariance Structures. 1869-1877 - Hanlin Goh, Nicolas Thome, Matthieu Cord, Joo-Hwee Lim:
Top-Down Regularization of Deep Belief Networks. 1878-1886 - Tamir Hazan, Subhransu Maji, Joseph Keshet, Tommi S. Jaakkola:
Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions. 1887-1895 - Yu Zhang:
Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms. 1896-1904 - Xiaojin Zhu:
Machine Teaching for Bayesian Learners in the Exponential Family. 1905-1913 - Qiang Liu, Alexander Ihler, Mark Steyvers:
Scoring Workers in Crowdsourcing: How Many Control Questions are Enough? 1914-1922 - Stefan Mathe, Cristian Sminchisescu:
Action from Still Image Dataset and Inverse Optimal Control to Learn Task Specific Visual Scanpaths. 1923-1931 - Jasper Snoek, Richard S. Zemel, Ryan Prescott Adams:
A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data. 1932-1940 - Fang Han, Han Liu:
Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model. 1941-1949 - Bogdan Savchynskyy, Jörg Hendrik Kappes, Paul Swoboda, Christoph Schnörr:
Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. 1950-1958 - James Sharpnack, Akshay Krishnamurthy, Aarti Singh:
Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic. 1959-1967 - Agnieszka Grabska-Barwinska, Jeffrey M. Beck, Alexandre Pouget, Peter E. Latham:
Demixing odors - fast inference in olfaction. 1968-1976 - Daniel Vainsencher, Shie Mannor, Huan Xu:
Learning Multiple Models via Regularized Weighting. 1977-1985 - Anima Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade:
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity. 1986-1994 - Maria-Florina Balcan, Steven Ehrlich, Yingyu Liang:
Distributed k-means and k-median clustering on general communication topologies. 1995-2003 - Kevin Swersky, Jasper Snoek, Ryan Prescott Adams:
Multi-Task Bayesian Optimization. 2004-2012 - Shahin Shahrampour, Alexander Rakhlin, Ali Jadbabaie:
Online Learning of Dynamic Parameters in Social Networks. 2013-2021 - Jinwoo Shin, Andrew E. Gelfand, Michael Chertkov:
A Graphical Transformation for Belief Propagation: Maximum Weight Matchings and Odd-Sized Cycles. 2022-2030 - Xinhua Zhang, Wee Sun Lee, Yee Whye Teh:
Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space. 2031-2039 - Andreas Ruttor, Philipp Batz, Manfred Opper:
Approximate Gaussian process inference for the drift function in stochastic differential equations. 2040-2048 - Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause:
Distributed Submodular Maximization: Identifying Representative Elements in Massive Data. 2049-2057 - Jacob D. Abernethy, Satyen Kale:
Adaptive Market Making via Online Learning. 2058-2066 - Alessandro Rudi, Guillermo D. Cañas, Lorenzo Rosasco:
On the Sample Complexity of Subspace Learning. 2067-2075 - Karin C. Knudson, Jonathan W. Pillow:
Spike train entropy-rate estimation using hierarchical Dirichlet process priors. 2076-2084 - Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Embed and Project: Discrete Sampling with Universal Hashing. 2085-2093 - Nitish Srivastava, Ruslan Salakhutdinov:
Discriminative Transfer Learning with Tree-based Priors. 2094-2102 - Anirban Roychowdhury, Ke Jiang, Brian Kulis:
Small-Variance Asymptotics for Hidden Markov Models. 2103-2111 - Viliam Lisý, Vojtech Kovarík, Marc Lanctot, Branislav Bosanský:
Convergence of Monte Carlo Tree Search in Simultaneous Move Games. 2112-2120 - Andrea Frome, Gregory S. Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc'Aurelio Ranzato, Tomás Mikolov:
DeViSE: A Deep Visual-Semantic Embedding Model. 2121-2129 - Xiaoxiao Guo, Satinder Singh, Richard L. Lewis:
Reward Mapping for Transfer in Long-Lived Agents. 2130-2138 - Martin Azizyan, Aarti Singh, Larry A. Wasserman:
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation. 2139-2147 - Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas:
Predicting Parameters in Deep Learning. 2148-2156 - Paul Valiant, Gregory Valiant:
Estimating the Unseen: Improved Estimators for Entropy and other Properties. 2157-2165 - Behzad Golshan, John W. Byers, Evimaria Terzi:
What do row and column marginals reveal about your dataset? 2166-2174 - Benigno Uria, Iain Murray, Hugo Larochelle:
RNADE: The real-valued neural autoregressive density-estimator. 2175-2183 - Thomas Bonald, Alexandre Proutière:
Two-Target Algorithms for Infinite-Armed Bandits with Bernoulli Rewards. 2184-2192 - Rémi Gribonval, Pierre Machart:
Reconciling "priors" & "priors" without prejudice? 2193-2201 - Nikhil S. Rao, Christopher R. Cox, Robert D. Nowak, Timothy T. Rogers:
Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis. 2202-2210 - Daniel S. Levine, Jonathan P. How:
Sensor Selection in High-Dimensional Gaussian Trees with Nuisances. 2211-2219 - Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill:
Sequential Transfer in Multi-armed Bandit with Finite Set of Models. 2220-2228 - Liu Yang, Jaime G. Carbonell:
Buy-in-Bulk Active Learning. 2229-2237 - James Y. Zou, Daniel J. Hsu, David C. Parkes, Ryan Prescott Adams:
Contrastive Learning Using Spectral Methods. 2238-2246 - Nils Napp, Ryan Prescott Adams:
Message Passing Inference with Chemical Reaction Networks. 2247-2255 - Daniel Russo, Benjamin Van Roy:
Eluder Dimension and the Sample Complexity of Optimistic Exploration. 2256-2264 - Andriy Mnih, Koray Kavukcuoglu:
Learning word embeddings efficiently with noise-contrastive estimation. 2265-2273 - Tuo Zhao, Han Liu:
Sparse Inverse Covariance Estimation with Calibration. 2274-2282 - Julien Mairal:
Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization. 2283-2291 - Marco Cuturi:
Sinkhorn Distances: Lightspeed Computation of Optimal Transport. 2292-2300 - Miao Xu, Rong Jin, Zhi-Hua Zhou:
Speedup Matrix Completion with Side Information: Application to Multi-Label Learning. 2301-2309 - Rupesh Kumar Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino J. Gomez, Jürgen Schmidhuber:
Compete to Compute. 2310-2318 - Byungkon Kang:
Fast Determinantal Point Process Sampling with Application to Clustering. 2319-2327 - Yuchen Zhang, John C. Duchi, Michael I. Jordan, Martin J. Wainwright:
Information-theoretic lower bounds for distributed statistical estimation with communication constraints. 2328-2336 - Philip S. Thomas, William Dabney, Stephen Giguere, Sridhar Mahadevan:
Projected Natural Actor-Critic. 2337-2345 - Jacob D. Abernethy, Peter L. Bartlett, Rafael M. Frongillo, Andre Wibisono:
How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal. 2346-2354 - Yacine Jernite, Yonatan Halpern, David A. Sontag:
Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests. 2355-2363 - Franz J. Király, Louis Theran:
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion. 2364-2372 - Dan Rosenbaum, Daniel Zoran, Yair Weiss:
Learning the Local Statistics of Optical Flow. 2373-2381 - Gunnar Kedenburg, Raphaël Fonteneau, Rémi Munos:
Aggregating Optimistic Planning Trees for Solving Markov Decision Processes. 2382-2390 - David Pfau, Eftychios A. Pnevmatikakis, Liam Paninski:
Robust learning of low-dimensional dynamics from large neural ensembles. 2391-2399 - Min Xu, Tao Qin, Tie-Yan Liu:
Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising. 2400-2408 - Nataliya Shapovalova, Michalis Raptis, Leonid Sigal, Greg Mori:
Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization. 2409-2417 - Jing Xiang, Seyoung Kim:
A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables. 2418-2426 - Matthias Hein, Simon Setzer, Leonardo Jost, Syama Sundar Rangapuram:
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited. 2427-2435 - Rishabh K. Iyer, Jeff A. Bilmes:
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints. 2436-2444 - Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng, Bo Zhang:
Scalable Inference for Logistic-Normal Topic Models. 2445-2453 - Il Memming Park, Evan Archer, Nicholas Priebe, Jonathan W. Pillow:
Spectral methods for neural characterization using generalized quadratic models. 2454-2462 - Il Memming Park, Evan Archer, Kenneth W. Latimer, Jonathan W. Pillow:
Universal models for binary spike patterns using centered Dirichlet processes. 2463-2471 - Tuan Anh Nguyen, Subbarao Kambhampati, Minh Binh Do:
Synthesizing Robust Plans under Incomplete Domain Models. 2472-2480 - Shaobo Han, Xuejun Liao, Lawrence Carin:
Integrated Non-Factorized Variational Inference. 2481-2489 - Ari Pakman, Liam Paninski:
Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions. 2490-2498 - Aswin Raghavan, Roni Khardon, Alan Fern, Prasad Tadepalli:
Symbolic Opportunistic Policy Iteration for Factored-Action MDPs. 2499-2507 - Yasin Abbasi-Yadkori, Peter L. Bartlett, Varun Kanade, Yevgeny Seldin, Csaba Szepesvári:
Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions. 2508-2516 - Alfredo A. Kalaitzis, Ricardo Bezerra de Andrade e Silva:
Flexible sampling of discrete data correlations without the marginal distributions. 2517-2525 - Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum:
One-shot learning by inverting a compositional causal process. 2526-2534 - Michel Besserve, Nikos K. Logothetis, Bernhard Schölkopf:
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. 2535-2543 - James R. Voss, Luis Rademacher, Mikhail Belkin:
Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis. 2544-2552 - Christian Szegedy, Alexander Toshev, Dumitru Erhan:
Deep Neural Networks for Object Detection. 2553-2561 - Suvrit Sra, Reshad Hosseini:
Geometric optimisation on positive definite matrices for elliptically contoured distributions. 2562-2570 - Ping Li, Gennady Samorodnitsky, John E. Hopcroft:
Sign Cauchy Projections and Chi-Square Kernel. 2571-2579 - Fang Zhao, Yongzhen Huang, Liang Wang, Tieniu Tan:
Relevance Topic Model for Unstructured Social Group Activity Recognition. 2580-2588 - Hu Ding, Ronald Berezney, Jinhui Xu:
k-Prototype Learning for 3D Rigid Structures. 2589-2597 - Sinead Williamson, Steven N. MacEachern, Eric P. Xing:
Restricting exchangeable nonparametric distributions. 2598-2606 - Shunan Zhang, Angela J. Yu:
Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting. 2607-2615 - Alexandros Paraschos, Christian Daniel, Jan Peters, Gerhard Neumann:
Probabilistic Movement Primitives. 2616-2624 - Shane Griffith, Kaushik Subramanian, Jonathan Scholz, Charles L. Isbell Jr., Andrea Lockerd Thomaz:
Policy Shaping: Integrating Human Feedback with Reinforcement Learning. 2625-2633 - Mark Rogers, Lei Li, Stuart Russell:
Multilinear Dynamical Systems for Tensor Time Series. 2634-2642 - Aäron van den Oord, Sander Dieleman, Benjamin Schrauwen:
Deep content-based music recommendation. 2643-2651 - Kamalika Chaudhuri, Staal Amund Vinterbo:
A Stability-based Validation Procedure for Differentially Private Machine Learning. 2652-2660 - Abbas Edalat:
Capacity of strong attractor patterns to model behavioural and cognitive prototypes. 2661-2669 - Vincent Q. Vu, Juhee Cho, Jing Lei, Karl Rohe:
Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA. 2670-2678 - Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman:
Cluster Trees on Manifolds. 2679-2687 - Mijung Park, Jonathan W. Pillow:
Bayesian inference for low rank spatiotemporal neural receptive fields. 2688-2696 - Victor Gabillon, Branislav Kveton, Zheng Wen, Brian Eriksson, S. Muthukrishnan:
Adaptive Submodular Maximization in Bandit Setting. 2697-2705 - Hossein Azari Soufiani, William Z. Chen, David C. Parkes, Lirong Xia:
Generalized Method-of-Moments for Rank Aggregation. 2706-2714 - Matthew J. Johnson, James Saunderson, Alan S. Willsky:
Analyzing Hogwild Parallel Gaussian Gibbs Sampling. 2715-2723 - H. Brendan McMahan, Jacob D. Abernethy:
Minimax Optimal Algorithms for Unconstrained Linear Optimization. 2724-2732 - Abhradeep Guha Thakurta, Adam D. Smith:
(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings. 2733-2741 - Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes:
Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions. 2742-2750 - Yifei Ma, Roman Garnett, Jeff G. Schneider:
Σ-Optimality for Active Learning on Gaussian Random Fields. 2751-2759 - Corinna Cortes, Marius Kloft, Mehryar Mohri:
Learning Kernels Using Local Rademacher Complexity. 2760-2768 - Roger B. Grosse, Chris J. Maddison, Ruslan Salakhutdinov:
Annealing between distributions by averaging moments. 2769-2777 - Robert V. Lindsey, Michael Mozer, William J. Huggins, Harold Pashler:
Optimizing Instructional Policies. 2778-2786 - Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, Oksana Yakhnenko:
Translating Embeddings for Modeling Multi-relational Data. 2787-2795 - Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi:
Phase Retrieval using Alternating Minimization. 2796-2804 - David E. Carlson, Vinayak A. Rao, Joshua T. Vogelstein, Lawrence Carin:
Real-Time Inference for a Gamma Process Model of Neural Spiking. 2805-2813 - Pierre Baldi, Peter J. Sadowski:
Understanding Dropout. 2814-2822 - Behnam Neyshabur, Nati Srebro, Ruslan Salakhutdinov, Yury Makarychev, Payman Yadollahpour:
The Power of Asymmetry in Binary Hashing. 2823-2831 - John C. Duchi, Michael I. Jordan, H. Brendan McMahan:
Estimation, Optimization, and Parallelism when Data is Sparse. 2832-2840 - Josh Merel, Roy Fox, Tony Jebara, Liam Paninski:
A multi-agent control framework for co-adaptation in brain-computer interfaces. 2841-2849 - Prem Gopalan, Chong Wang, David M. Blei:
Modeling Overlapping Communities with Node Popularities. 2850-2858 - Beomjoon Kim, Amir-massoud Farahmand, Joelle Pineau, Doina Precup:
Learning from Limited Demonstrations. 2859-2867 - Guy Van den Broeck, Adnan Darwiche:
On the Complexity and Approximation of Binary Evidence in Lifted Inference. 2868-2876 - James Martens, Arkadev Chattopadhyay, Toniann Pitassi, Richard S. Zemel:
On the Expressive Power of Restricted Boltzmann Machines. 2877-2885 - Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain:
Memory Limited, Streaming PCA. 2886-2894 - Srikrishna Sridhar, Stephen J. Wright, Christopher Ré, Ji Liu, Victor Bittorf, Ce Zhang:
An Approximate, Efficient LP Solver for LP Rounding. 2895-2903 - Özgür Simsek:
Linear decision rule as aspiration for simple decision heuristics. 2904-2912 - Harikrishna Narasimhan, Shivani Agarwal:
On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation. 2913-2921 - Arash A. Amini, XuanLong Nguyen:
Bayesian inference as iterated random functions with applications to sequential inference in graphical models. 2922-2930 - Hristo S. Paskov, Robert West, John C. Mitchell, Trevor J. Hastie:
Compressive Feature Learning. 2931-2939 - Matus Telgarsky, Sanjoy Dasgupta:
Moment-based Uniform Deviation Bounds for k-means and Friends. 2940-2948 - Mohammad Amin Sadeghi, David A. Forsyth:
Fast Template Evaluation with Vector Quantization. 2949-2957 - Sheeraz Ahmad, He Huang, Angela J. Yu:
Context-sensitive active sensing in humans. 2958-2966 - Bernardino Romera-Paredes, Massimiliano Pontil:
A New Convex Relaxation for Tensor Completion. 2967-2975 - Qiang Cheng, Qiang Liu, Feng Chen, Alexander Ihler:
Variational Planning for Graph-based MDPs. 2976-2984 - Özlem Aslan, Hao Cheng, Xinhua Zhang, Dale Schuurmans:
Convex Two-Layer Modeling. 2985-2993 - Haim Avron, Vikas Sindhwani, David P. Woodruff:
Sketching Structured Matrices for Faster Nonlinear Regression. 2994-3002 - Ian Osband, Daniel Russo, Benjamin Van Roy:
(More) Efficient Reinforcement Learning via Posterior Sampling. 3003-3011 - Adel Javanmard, Andrea Montanari:
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition. 3012-3020 - Zheng Wen, Benjamin Van Roy:
Efficient Exploration and Value Function Generalization in Deterministic Systems. 3021-3029 - Mahdi Milani Fard, Yuri Grinberg, Amir-massoud Farahmand, Joelle Pineau, Doina Precup:
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces. 3030-3038 - Nathaniel J. Smith, Noah D. Goodman, Michael C. Frank:
Learning and using language via recursive pragmatic reasoning about other agents. 3039-3047 - Andreas Stuhlmüller, Jessica Taylor, Noah D. Goodman:
Learning Stochastic Inverses. 3048-3056 - Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio:
Learning invariant representations and applications to face verification. 3057-3065 - Alexander Rakhlin, Karthik Sridharan:
Optimization, Learning, and Games with Predictable Sequences. 3066-3074 - Samory Kpotufe, Vikas K. Garg:
Adaptivity to Local Smoothness and Dimension in Kernel Regression. 3075-3083 - Lei Jimmy Ba, Brendan J. Frey:
Adaptive dropout for training deep neural networks. 3084-3092 - Daniel Yamins, Ha Hong, Charles F. Cadieu, James J. DiCarlo:
Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream. 3093-3101 - Sam Patterson, Yee Whye Teh:
Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex. 3102-3110 - Tomás Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, Jeffrey Dean:
Distributed Representations of Words and Phrases and their Compositionality. 3111-3119 - Tai Qin, Karl Rohe:
Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel. 3120-3128 - Xiao-Ming Wu, Zhenguo Li, Shih-Fu Chang:
Analyzing the Harmonic Structure in Graph-Based Learning. 3129-3137 - Marius Pachitariu, Biljana Petreska, Maneesh Sahani:
Recurrent linear models of simultaneously-recorded neural populations. 3138-3146 - Nan Du, Le Song, Manuel Gomez-Rodriguez, Hongyuan Zha:
Scalable Influence Estimation in Continuous-Time Diffusion Networks. 3147-3155 - Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen:
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC. 3156-3164 - Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar, Russell A. Poldrack:
BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables. 3165-3173 - Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund:
The Fast Convergence of Incremental PCA. 3174-3182 - Aurel A. Lazar, Yevgeniy B. Slutskiy:
Multisensory Encoding, Decoding, and Identification. 3183-3191 - Krzysztof Choromanski, Tony Jebara, Kui Tang:
Adaptive Anonymity via b-Matching. 3192-3200 - Matjaz Jogan, Alan A. Stocker:
Optimal integration of visual speed across different spatiotemporal frequency channels. 3201-3209 - Martin Slawski, Matthias Hein, Pavlo Lutsik:
Matrix factorization with binary components. 3210-3218 - Nicolas Heess, Daniel Tarlow, John M. Winn:
Learning to Pass Expectation Propagation Messages. 3219-3227 - Le Song, Bo Dai:
Robust Low Rank Kernel Embeddings of Multivariate Distributions. 3228-3236
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