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ORCID23. NIPS 2010: Vancouver, British Columbia, Canada
- John D. Lafferty, Christopher K. I. Williams, John Shawe-Taylor, Richard S. Zemel, Aron Culotta:
Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada. Curran Associates, Inc. 2010 - Jacob D. Abernethy, Manfred K. Warmuth:
Repeated Games against Budgeted Adversaries. 1-9 - Margareta Ackerman, Shai Ben-David, David Loker:
Towards Property-Based Classification of Clustering Paradigms. 10-18 - Ryan Prescott Adams, Zoubin Ghahramani, Michael I. Jordan:
Tree-Structured Stick Breaking for Hierarchical Data. 19-27 - Felix V. Agakov, Paul McKeigue, Jon Krohn, Amos J. Storkey:
Sparse Instrumental Variables (SPIV) for Genome-Wide Studies. 28-36 - Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright:
Fast global convergence rates of gradient methods for high-dimensional statistical recovery. 37-45 - Arvind Agarwal, Hal Daumé III, Samuel Gerber:
Learning Multiple Tasks using Manifold Regularization. 46-54 - Mauricio A. Álvarez, Jan Peters, Bernhard Schölkopf, Neil D. Lawrence:
Switched Latent Force Models for Movement Segmentation. 55-63 - Mauricio Araya-López, Olivier Buffet, Vincent Thomas, François Charpillet:
A POMDP Extension with Belief-dependent Rewards. 64-72 - Nimar S. Arora, Stuart Russell, Paul Kidwell, Erik B. Sudderth:
Global seismic monitoring as probabilistic inference. 73-81 - Joseph L. Austerweil, Thomas L. Griffiths:
Learning invariant features using the Transformed Indian Buffet Process. 82-90 - Pranjal Awasthi, Reza Bosagh Zadeh:
Supervised Clustering. 91-99 - Alper Ayvaci, Michalis Raptis, Stefano Soatto:
Occlusion Detection and Motion Estimation with Convex Optimization. 100-108 - Javad Azimi, Alan Fern, Xiaoli Z. Fern:
Batch Bayesian Optimization via Simulation Matching. 109-117 - Francis R. Bach:
Structured sparsity-inducing norms through submodular functions. 118-126 - Stephen H. Bach, Marcus A. Maloof:
A Bayesian Approach to Concept Drift. 127-135 - Chris Barber, Joseph Bockhorst, Paul Roebber:
Auto-Regressive HMM Inference with Incomplete Data for Short-Horizon Wind Forecasting. 136-144 - Mohsen Bayati, José Bento, Andrea Montanari:
The LASSO risk: asymptotic results and real world examples. 145-153 - Gowtham Bellala, Suresh K. Bhavnani, Clayton Scott:
Extensions of Generalized Binary Search to Group Identification and Exponential Costs. 154-162 - Samy Bengio, Jason Weston, David Grangier:
Label Embedding Trees for Large Multi-Class Tasks. 163-171 - Alessandro Bergamo, Lorenzo Torresani:
Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach. 181-189 - Andrey Bernstein, Shie Mannor, Nahum Shimkin:
Online Classification with Specificity Constraints. 190-198 - Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang:
Agnostic Active Learning Without Constraints. 199-207 - Danny Bickson, Carlos Guestrin:
Inference with Multivariate Heavy-Tails in Linear Models. 208-216 - Jacob Bien, Ya Xu, Michael W. Mahoney:
CUR from a Sparse Optimization Viewpoint. 217-225 - Gilles Blanchard, Nicole Krämer:
Optimal learning rates for Kernel Conjugate Gradient regression. 226-234 - Matthew B. Blaschko, Andrea Vedaldi, Andrew Zisserman:
Simultaneous Object Detection and Ranking with Weak Supervision. 235-243 - Liefeng Bo, Xiaofeng Ren, Dieter Fox:
Kernel Descriptors for Visual Recognition. 244-252 - Sander M. Bohté, Jaldert O. Rombouts:
Fractionally Predictive Spiking Neurons. 253-261 - Edwin V. Bonilla, Shengbo Guo, Scott Sanner:
Gaussian Process Preference Elicitation. 262-270 - Byron Boots, Geoffrey J. Gordon:
Predictive State Temporal Difference Learning. 271-279 - Alexandre Bouchard-Côté, Michael I. Jordan:
Variational Inference over Combinatorial Spaces. 280-288 - Abdeslam Boularias, Brahim Chaib-draa:
Bootstrapping Apprenticeship Learning. 289-297 - Christos Boutsidis, Anastasios Zouzias, Petros Drineas
:
Random Projections for $k$-means Clustering. 298-306 - William Brendel, Sinisa Todorovic:
Segmentation as Maximum-Weight Independent Set. 307-315 - Matthias Broecheler, Lise Getoor:
Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning. 316-324 - Serhat Selcuk Bucak, Rong Jin, Anil K. Jain:
Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition. 325-333 - America Chambers, Padhraic Smyth, Mark Steyvers:
Learning concept graphs from text with stick-breaking priors. 334-342 - Kamalika Chaudhuri, Sanjoy Dasgupta:
Rates of convergence for the cluster tree. 343-351 - Anton Chechetka, Carlos Guestrin:
Evidence-Specific Structures for Rich Tractable CRFs. 352-360 - Ning Chen, Jun Zhu, Eric P. Xing:
Predictive Subspace Learning for Multi-view Data: a Large Margin Approach. 361-369 - Wei Chen, Tie-Yan Liu, Zhiming Ma:
Two-Layer Generalization Analysis for Ranking Using Rademacher Average. 370-378 - Sylvain Chevallier, Hélène Paugam-Moisy, Michèle Sebag:
SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system. 379-387 - Silvia Chiappa, Jan Peters:
Movement extraction by detecting dynamics switches and repetitions. 388-396 - Alessandro Chiuso, Gianluigi Pillonetto:
Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors. 397-405 - Andreas Christmann, Ingo Steinwart:
Universal Kernels on Non-Standard Input Spaces. 406-414 - Tom Claassen, Tom Heskes:
Causal discovery in multiple models from different experiments. 415-423 - Shay B. Cohen, Noah A. Smith:
Empirical Risk Minimization with Approximations of Probabilistic Grammars. 424-432 - Elaine A. Corbett, Eric J. Perreault, Konrad P. Körding:
Mixture of time-warped trajectory models for movement decoding. 433-441 - Corinna Cortes, Yishay Mansour, Mehryar Mohri:
Learning Bounds for Importance Weighting. 442-450 - Koby Crammer, Daniel D. Lee:
Learning via Gaussian Herding. 451-459 - Rémi Cuingnet, Marie Chupin, Habib Benali, Olivier Colliot:
Spatial and anatomical regularization of SVM for brain image analysis. 460-468 - George E. Dahl, Marc'Aurelio Ranzato, Abdel-rahman Mohamed, Geoffrey E. Hinton:
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine. 469-477 - Aman Dhesi, Purushottam Kar:
Random Projection Trees Revisited. 496-504 - Uwe Dick, Peter Haider, Thomas Vanck, Michael Brückner, Tobias Scheffer:
Throttling Poisson Processes. 505-513 - Nan Ding, S. V. N. Vishwanathan:
t-logistic regression. 514-522 - Justin Domke:
Implicit Differentiation by Perturbation. 523-531 - Finale Doshi-Velez, David Wingate, Nicholas Roy, Joshua B. Tenenbaum:
Nonparametric Bayesian Policy Priors for Reinforcement Learning. 532-540 - Shaul Druckmann, Dmitri B. Chklovskii:
Over-complete representations on recurrent neural networks can support persistent percepts. 541-549 - John C. Duchi, Alekh Agarwal, Martin J. Wainwright:
Distributed Dual Averaging In Networks. 550-558 - Gal Elidan:
Copula Bayesian Networks. 559-567 - Amir Massoud Farahmand, Rémi Munos, Csaba Szepesvári:
Error Propagation for Approximate Policy and Value Iteration. 568-576 - Mahdi Milani Fard, Joelle Pineau:
PAC-Bayesian Model Selection for Reinforcement Learning. 1624-1632 - Alan Fern, Prasad Tadepalli:
A Computational Decision Theory for Interactive Assistants. 577-585 - Sarah Filippi, Olivier Cappé, Aurélien Garivier, Csaba Szepesvári:
Parametric Bandits: The Generalized Linear Case. 586-594 - Nicholas Fisher, Arunava Banerjee:
A Novel Kernel for Learning a Neuron Model from Spike Train Data. 595-603 - Rina Foygel, Mathias Drton:
Extended Bayesian Information Criteria for Gaussian Graphical Models. 604-612 - Bela A. Frigyik, Maya R. Gupta, Yihua Chen:
Shadow Dirichlet for Restricted Probability Modeling. 613-621 - Mario Fritz, Kate Saenko, Trevor Darrell:
Size Matters: Metric Visual Search Constraints from Monocular Metadata. 622-630 - Vicky Froyen, Jacob Feldman, Manish Singh:
A Bayesian Framework for Figure-Ground Interpretation. 631-639 - C. C. Alan Fung, K. Y. Michael Wong, He Wang, Si Wu:
Attractor Dynamics with Synaptic Depression. 640-648 - Kun Gai, Guangyun Chen, Changshui Zhang:
Learning Kernels with Radiuses of Minimum Enclosing Balls. 649-657 - Surya Ganguli, Haim Sompolinsky:
Short-term memory in neuronal networks through dynamical compressed sensing. 667-675 - Deep Ganguli, Eero P. Simoncelli:
Implicit encoding of prior probabilities in optimal neural populations. 658-666 - Pierre Garrigues, Bruno A. Olshausen:
Group Sparse Coding with a Laplacian Scale Mixture Prior. 676-684 - Jan Gasthaus, Yee Whye Teh:
Improvements to the Sequence Memoizer. 685-693 - Andrew Gelfand, Yutian Chen, Laurens van der Maaten, Max Welling:
On Herding and the Perceptron Cycling Theorem. 694-702 - Felipe Gerhard, Wulfram Gerstner:
Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models. 703-711 - Samuel Gershman, Robert Wilson:
The Neural Costs of Optimal Control. 712-720 - Mohammad Ghavamzadeh, Alessandro Lazaric, Odalric-Ambrym Maillard, Rémi Munos:
LSTD with Random Projections. 721-729 - Bryan R. Gibson, Xiaojin Zhu, Timothy T. Rogers, Chuck Kalish, Joseph Harrison:
Humans Learn Using Manifolds, Reluctantly. 730-738 - Tobias Glasmachers:
Universal Consistency of Multi-Class Support Vector Classification. 739-747 - Vibhav Gogate, William Austin Webb, Pedro M. Domingos:
Learning Efficient Markov Networks. 748-756 - Andrew B. Goldberg, Xiaojin Zhu, Ben Recht, Jun-Ming Xu, Robert D. Nowak:
Transduction with Matrix Completion: Three Birds with One Stone. 757-765 - Daniel Golovin, Andreas Krause, Debajyoti Ray:
Near-Optimal Bayesian Active Learning with Noisy Observations. 766-774 - Ryan Gomes, Andreas Krause, Pietro Perona:
Discriminative Clustering by Regularized Information Maximization. 775-783 - Dan F. M. Goodman, Romain Brette:
Learning to localise sounds with spiking neural networks. 784-792 - David Grangier, Iain Melvin:
Feature Set Embedding for Incomplete Data. 793-801 - Yuhong Guo:
Active Instance Sampling via Matrix Partition. 802-810 - Yanjun Han, Qing Tao, Jue Wang:
Avoiding False Positive in Multi-Instance Learning. 811-819 - Lauren Hannah, Warren B. Powell, David M. Blei:
Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable. 820-828 - Stefan Harmeling, Michael Hirsch, Bernhard Schölkopf:
Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake. 829-837 - Hado van Hasselt:
Double Q-learning. 2613-2621 - Tamir Hazan, Raquel Urtasun:
A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction. 838-846 - Matthias Hein, Thomas Bühler:
An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA. 847-855 - Matthew D. Hoffman, David M. Blei, Francis R. Bach:
Online Learning for Latent Dirichlet Allocation. 856-864 - Diane Hu, Laurens van der Maaten, Youngmin Cho, Lawrence K. Saul, Sorin Lerner:
Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development. 865-873 - Ling Huang, Jinzhu Jia, Bin Yu, Byung-Gon Chun, Petros Maniatis, Mayur Naik:
Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression. 883-891 - Sheng-Jun Huang, Rong Jin, Zhi-Hua Zhou:
Active Learning by Querying Informative and Representative Examples. 892-900 - Jim C. Huang, Nebojsa Jojic, Christopher Meek:
Exact inference and learning for cumulative distribution functions on loopy graphs. 874-882 - Dirk Husmeier, Frank Dondelinger, Sophie Lèbre:
Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks. 901-909 - Hal Daumé III, Abhishek Kumar, Avishek Saha:
Co-regularization Based Semi-supervised Domain Adaptation. 478-486 - Guy Isley, Christopher J. Hillar, Friedrich T. Sommer:
Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication. 910-918 - Katsuhiko Ishiguro, Tomoharu Iwata, Naonori Ueda, Joshua B. Tenenbaum:
Dynamic Infinite Relational Model for Time-varying Relational Data Analysis. 919-927 - Prateek Jain, Brian Kulis, Inderjit S. Dhillon:
Inductive Regularized Learning of Kernel Functions. 946-954 - Prateek Jain, Sudheendra Vijayanarasimhan, Kristen Grauman:
Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning. 928-936 - Prateek Jain, Raghu Meka, Inderjit S. Dhillon:
Guaranteed Rank Minimization via Singular Value Projection. 937-945 - Ali Jalali, Pradeep Ravikumar, Sujay Sanghavi, Chao Ruan:
A Dirty Model for Multi-task Learning. 964-972 - Abhay Kumar Jha, Vibhav Gogate, Alexandra Meliou, Dan Suciu:
Lifted Inference Seen from the Other Side : The Tractable Features. 973-981 - Yangqing Jia, Mathieu Salzmann, Trevor Darrell:
Factorized Latent Spaces with Structured Sparsity. 982-990 - Albert Xin Jiang, Kevin Leyton-Brown:
Bayesian Action-Graph Games. 991-999 - Jie Tang, Pieter Abbeel:
On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient. 1000-1008 - Jeffrey Johns, Christopher Painter-Wakefield, Ronald Parr:
Linear Complementarity for Regularized Policy Evaluation and Improvement. 1009-1017 - Mark Johnson, Katherine Demuth, Michael C. Frank, Bevan K. Jones:
Synergies in learning words and their referents. 1018-1026 - Nebojsa Jojic, Alessandro Perina, Vittorio Murino:
Structural epitome: a way to summarize one's visual experience. 1027-1035 - Peter B. Jones, Venkatesh Saligrama, Sanjoy K. Mitter:
Probabilistic Belief Revision with Structural Constraints. 1036-1044 - Armand Joulin, Francis R. Bach, Jean Ponce:
Efficient Optimization for Discriminative Latent Class Models. 1045-1053 - Satyen Kale, Lev Reyzin, Robert E. Schapire:
Non-Stochastic Bandit Slate Problems. 1054-1062 - Nikos Karampatziakis:
Static Analysis of Binary Executables Using Structural SVMs. 1063-1071 - Leonid Karlinsky, Michael Dinerstein, Shimon Ullman:
Using body-anchored priors for identifying actions in single images. 1072-1080 - Kentaro Katahira, Kazuo Okanoya, Masato Okada:
Effects of Synaptic Weight Diffusion on Learning in Decision Making Networks. 1081-1089 - Koray Kavukcuoglu, Pierre Sermanet, Y-Lan Boureau, Karol Gregor, Michaël Mathieu, Yann LeCun:
Learning Convolutional Feature Hierarchies for Visual Recognition. 1090-1098 - Ryan C. Kelly, Matthew A. Smith, Robert E. Kass, Tai Sing Lee:
Accounting for network effects in neuronal responses using L1 regularized point process models. 1099-1107 - Mohammad Emtiyaz Khan, Benjamin M. Marlin, Guillaume Bouchard, Kevin P. Murphy:
Variational bounds for mixed-data factor analysis. 1108-1116 - Taehwan Kim, Gregory Shakhnarovich, Raquel Urtasun:
Sparse Coding for Learning Interpretable Spatio-Temporal Primitives. 1117-1125 - Diederik P. Kingma, Yann LeCun:
Regularized estimation of image statistics by Score Matching. 1126-1134 - Ariel Kleiner, Ali Rahimi, Michael I. Jordan:
Random Conic Pursuit for Semidefinite Programming. 1135-1143 - Vladimir Kolmogorov:
Generalized roof duality and bisubmodular functions. 1144-1152 - J. Zico Kolter, Siddharth Batra, Andrew Y. Ng:
Energy Disaggregation via Discriminative Sparse Coding. 1153-1161 - George Dimitri Konidaris, Scott Kuindersma, Andrew G. Barto, Roderic A. Grupen:
Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories. 1162-1170 - Alex Kulesza, Ben Taskar:
Structured Determinantal Point Processes. 1171-1179 - Akshat Kumar, Shlomo Zilberstein:
MAP Estimation for Graphical Models by Likelihood Maximization. 1180-1188 - M. Pawan Kumar, Benjamin Packer, Daphne Koller:
Self-Paced Learning for Latent Variable Models. 1189-1197 - Achintya Kundu, Vikram Tankasali, Chiranjib Bhattacharyya, Aharon Ben-Tal:
Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions. 1198-1206 - Tian Lan, Yang Wang, Weilong Yang, Greg Mori:
Beyond Actions: Discriminative Models for Contextual Group Activities. 1216-1224 - Georg Langs, Yanmei Tie, Laura Rigolo, Alexandra J. Golby, Polina Golland:
Functional Geometry Alignment and Localization of Brain Areas. 1225-1233 - Ni Lao, Jun Zhu, Xinwang Liu, Yandong Liu, William W. Cohen:
Efficient Relational Learning with Hidden Variable Detection. 1234-1242 - Hugo Larochelle, Geoffrey E. Hinton:
Learning to combine foveal glimpses with a third-order Boltzmann machine. 1243-1251 - Danial Lashkari, Ramesh Sridharan, Polina Golland:
Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. 1252-1260 - Aurel A. Lazar, Yevgeniy B. Slutskiy:
Identifying Dendritic Processing. 1261-1269 - Quoc V. Le, Jiquan Ngiam, Zhenghao Chen, Daniel Jin hao Chia, Pang Wei Koh, Andrew Y. Ng:
Tiled convolutional neural networks. 1279-1287 - Hai-Son Le, Ziv Bar-Joseph:
Cross Species Expression Analysis using a Dirichlet Process Mixture Model with Latent Matchings. 1270-1278 - Chang Su, Sargur N. Srihari:
Evaluation of Rarity of Fingerprints in Forensics. 1207-1215 - David C. Lee, Abhinav Gupta, Martial Hebert, Takeo Kanade:
Estimating Spatial Layout of Rooms using Volumetric Reasoning about Objects and Surfaces. 1288-1296 - Seunghak Lee, Jun Zhu, Eric P. Xing:
Adaptive Multi-Task Lasso: with Application to eQTL Detection. 1306-1314 - Jason D. Lee, Ben Recht, Ruslan Salakhutdinov, Nathan Srebro, Joel A. Tropp:
Practical Large-Scale Optimization for Max-norm Regularization. 1297-1305 - Leonidas Lefakis, François Fleuret:
Joint Cascade Optimization Using A Product Of Boosted Classifiers. 1315-1323 - Victor S. Lempitsky, Andrew Zisserman:
Learning To Count Objects in Images. 1324-1332 - Gilbert Leung, Novi Quadrianto, Alexander J. Smola, Kostas Tsioutsiouliklis:
Optimal Web-Scale Tiering as a Flow Problem. 1333-1341 - Sergey Levine, Zoran Popovic, Vladlen Koltun:
Feature Construction for Inverse Reinforcement Learning. 1342-1350 - Fuxin Li, Cristian Sminchisescu:
Convex Multiple-Instance Learning by Estimating Likelihood Ratio. 1360-1368 - Li-Jia Li, Hao Su, Eric P. Xing, Li Fei-Fei:
Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification. 1378-1386 - Kaiming Li, Lei Guo, Carlos Faraco, Dajiang Zhu, Fan Deng, Tuo Zhang, Xi Jiang, Degang Zhang, Hanbo Chen, Xintao Hu, L. Stephen Miller, Tianming Liu:
Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles. 1369-1377 - Congcong Li, Adarsh Kowdle, Ashutosh Saxena, Tsuhan Chen:
Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models. 1351-1359 - Ping Li, Arnd Christian König, Wenhao Gui:
b-Bit Minwise Hashing for Estimating Three-Way Similarities. 1387-1395 - Dahua Lin, Eric Grimson, John W. Fisher III:
Construction of Dependent Dirichlet Processes based on Poisson Processes. 1396-1404 - Yuanqing Lin, Tong Zhang, Shenghuo Zhu, Kai Yu:
Deep Coding Network. 1405-1413 - Jun Liu, Jieping Ye:
Moreau-Yosida Regularization for Grouped Tree Structure Learning. 1459-1467 - Han Liu, Kathryn Roeder, Larry A. Wasserman:
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models. 1432-1440 - Han Liu, Xi Chen:
Multivariate Dyadic Regression Trees for Sparse Learning Problems. 1441-1449 - Hairong Liu, Longin Jan Latecki, Shuicheng Yan:
Robust Clustering as Ensembles of Affinity Relations. 1414-1422 - Han Liu, Xi Chen, John D. Lafferty, Larry A. Wasserman:
Graph-Valued Regression. 1423-1431 - Ji Liu, Peter Wonka, Jieping Ye:
Multi-Stage Dantzig Selector. 1450-1458 - Yuzong Liu, Mohit Sharma, Charles M. Gaona, Jonathan Breshears, Jarod Roland, Zachary Freudenburg, Kilian Q. Weinberger, Eric Leuthardt:
Decoding Ipsilateral Finger Movements from ECoG Signals in Humans. 1468-1476 - Daniel Lowd, Pedro M. Domingos:
Approximate Inference by Compilation to Arithmetic Circuits. 1477-1485 - Aurélie C. Lozano, Vikas Sindhwani:
Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference. 1486-1494 - Hongjing Lu, Tungyou Lin, Alan L. F. Lee, Luminita A. Vese, Alan L. Yuille:
Functional form of motion priors in human motion perception. 1495-1503 - Jie Luo, Francesco Orabona:
Learning from Candidate Labeling Sets. 1504-1512 - Ronny Luss, Saharon Rosset, Moni Shahar:
Decomposing Isotonic Regression for Efficiently Solving Large Problems. 1513-1521 - Ulrike von Luxburg, Agnes Radl, Matthias Hein:
Getting lost in space: Large sample analysis of the resistance distance. 2622-2630 - Siwei Lyu:
Divisive Normalization: Justification and Effectiveness as Efficient Coding Transform. 1522-1530 - Malik Magdon-Ismail:
Permutation Complexity Bound on Out-Sample Error. 1531-1539 - Sridhar Mahadevan, Bo Liu:
Basis Construction from Power Series Expansions of Value Functions. 1540-1548 - Odalric-Ambrym Maillard, Rémi Munos:
Scrambled Objects for Least-Squares Regression. 1549-1557 - Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis R. Bach:
Network Flow Algorithms for Structured Sparsity. 1558-1566 - Yariv Maron, Michael Lamar, Elie Bienenstock:
Sphere Embedding: An Application to Part-of-Speech Induction. 1567-1575 - Hamed Masnadi-Shirazi, Nuno Vasconcelos:
Variable margin losses for classifier design. 1576-1584 - Luke Maurits, Daniel J. Navarro, Amy Perfors:
Why are some word orders more common than others? A uniform information density account. 1585-1593 - David A. McAllester, Tamir Hazan, Joseph Keshet:
Direct Loss Minimization for Structured Prediction. 1594-1602 - Roland Memisevic, Christopher Zach, Geoffrey E. Hinton, Marc Pollefeys:
Gated Softmax Classification. 1603-1611 - Charles A. Micchelli, Jean Morales, Massimiliano Pontil:
A Family of Penalty Functions for Structured Sparsity. 1612-1623 - Benjamin A. Miller, Nadya T. Bliss, Patrick J. Wolfe:
Subgraph Detection Using Eigenvector L1 Norms. 1633-1641 - Sebastian Millner, Andreas Grübl, Karlheinz Meier, Johannes Schemmel, Marc-Olivier Schwartz:
A VLSI Implementation of the Adaptive Exponential Integrate-and-Fire Neuron Model. 1642-1650 - Kaushik Mitra, Sameer Sheorey, Rama Chellappa:
Large-Scale Matrix Factorization with Missing Data under Additional Constraints. 1651-1659 - Atsushi Miyamae, Yuichi Nagata, Isao Ono, Shigenobu Kobayashi:
Natural Policy Gradient Methods with Parameter-based Exploration for Control Tasks. 1660-1668 - Eiji Mizutani, Stuart Dreyfus:
An analysis on negative curvature induced by singularity in multi-layer neural-network learning. 1669-1677 - Grégoire Montavon, Mikio L. Braun, Klaus-Robert Müller:
Layer-wise analysis of deep networks with Gaussian kernels. 1678-1686 - Joris M. Mooij, Oliver Stegle, Dominik Janzing, Kun Zhang, Bernhard Schölkopf:
Probabilistic latent variable models for distinguishing between cause and effect. 1687-1695 - Sofia Mosci, Silvia Villa, Alessandro Verri, Lorenzo Rosasco:
A Primal-Dual Algorithm for Group Sparse Regularization with Overlapping Groups. 2604-2612 - Kamiya Motwani, Nagesh Adluru, Chris Hinrichs, Andrew L. Alexander, Vikas Singh:
Epitome driven 3-D Diffusion Tensor image segmentation: on extracting specific structures. 1696-1704 - Michael Mozer, Harold Pashler, Matthew H. Wilder, Robert V. Lindsey, Matt Jones, Michael Jones:
Improving Human Judgments by Decontaminating Sequential Dependencies. 1705-1713 - Indraneel Mukherjee, Robert E. Schapire:
A Theory of Multiclass Boosting. 1714-1722 - Kritika Muralidharan, Nuno Vasconcelos:
A biologically plausible network for the computation of orientation dominance. 1723-1731 - Iain Murray, Ryan Prescott Adams:
Slice sampling covariance hyperparameters of latent Gaussian models. 1732-1740 - Seth A. Myers, Jure Leskovec:
On the Convexity of Latent Social Network Inference. 1741-1749 - Morten Mørup, Kristoffer Hougaard Madsen, Anne-Marie Dogonowski, Hartwig R. Siebner, Lars Kai Hansen:
Infinite Relational Modeling of Functional Connectivity in Resting State fMRI. 1750-1758 - Kiyohito Nagano, Yoshinobu Kawahara, Satoru Iwata:
Minimum Average Cost Clustering. 1759-1767 - Shinichi Nakajima, Masashi Sugiyama, Ryota Tomioka:
Global Analytic Solution for Variational Bayesian Matrix Factorization. 1768-1776 - Hariharan Narayanan, Alexander Rakhlin:
Random Walk Approach to Regret Minimization. 1777-1785 - Hariharan Narayanan, Sanjoy K. Mitter:
Sample Complexity of Testing the Manifold Hypothesis. 1786-1794 - Daniel J. Navarro:
Learning the context of a category. 1795-1803 - Gergely Neu, András György, Csaba Szepesvári, András Antos:
Online Markov Decision Processes under Bandit Feedback. 1804-1812 - Feiping Nie, Heng Huang, Xiao Cai, Chris H. Q. Ding:
Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization. 1813-1821 - Yung-Kyun Noh, Byoung-Tak Zhang, Daniel D. Lee:
Generative Local Metric Learning for Nearest Neighbor Classification. 1822-1830 - Manfred Opper, Andreas Ruttor, Guido Sanguinetti:
Approximate inference in continuous time Gaussian-Jump processes. 1831-1839 - Francesco Orabona, Koby Crammer:
New Adaptive Algorithms for Online Classification. 1840-1848 - Dávid Pál, Barnabás Póczos, Csaba Szepesvári:
Estimation of Renyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs. 1849-1857 - George Papandreou, Alan L. Yuille:
Gaussian sampling by local perturbations. 1858-1866 - Shibin Parameswaran, Kilian Q. Weinberger:
Large Margin Multi-Task Metric Learning. 1867-1875 - Manas A. Pathak, Shantanu Rane, Bhiksha Raj:
Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers. 1876-1884 - Nadia Payet, Sinisa Todorovic:
(RF)^2 - Random Forest Random Field. 1885-1893 - Dmitry Pechyony, Vladimir Vapnik:
On the Theory of Learnining with Privileged Information. 1894-1902 - Thomas Peel, Sandrine Anthoine, Liva Ralaivola:
Empirical Bernstein Inequalities for U-Statistics. 1903-1911 - José Bento, Morteza Ibrahimi, Andrea Montanari:
Learning Networks of Stochastic Differential Equations. 172-180 - James Petterson, Alexander J. Smola, Tibério S. Caetano, Wray L. Buntine, Shravan M. Narayanamurthy:
Word Features for Latent Dirichlet Allocation. 1921-1929 - James Petterson, Tibério S. Caetano:
Reverse Multi-Label Learning. 1912-1920 - David Pfau, Nicholas Bartlett, Frank D. Wood:
Probabilistic Deterministic Infinite Automata. 1930-1938 - Gervasio Puertas, Jörg Bornschein, Jörg Lücke:
The Maximal Causes of Natural Scenes are Edge Filters. 1939-1947 - Tao Qin, Xiubo Geng, Tie-Yan Liu:
A New Probabilistic Model for Rank Aggregation. 1948-1956 - Novi Quadrianto, Alexander J. Smola, Tibério S. Caetano, S. V. N. Vishwanathan, James Petterson:
Multitask Learning without Label Correspondences. 1957-1965 - Kanaka Rajan, L. F. Abbott, Haim Sompolinsky:
Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics. 1975-1983 - Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Random Averages, Combinatorial Parameters, and Learnability. 1984-1992 - Ralf M. Haefner, Matthias Bethge:
Evaluating neuronal codes for inference using Fisher information. 1993-2001 - Marc'Aurelio Ranzato, Volodymyr Mnih, Geoffrey E. Hinton:
Generating more realistic images using gated MRF's. 2002-2010 - Konrad Rawlik, Marc Toussaint, Sethu Vijayakumar:
An Approximate Inference Approach to Temporal Optimization in Optimal Control. 2011-2019 - Sashank Jakkam Reddi, Sunita Sarawagi, Sundar Vishwanathan:
MAP estimation in Binary MRFs via Bipartite Multi-cuts. 955-963 - David P. Reichert, Peggy Seriès, Amos J. Storkey:
Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model. 2020-2028 - Emile Richard, Nicolas Baskiotis, Theodoros Evgeniou, Nicolas Vayatis:
Link Discovery using Graph Feature Tracking. 1966-1974 - Paul Ruvolo, Javier R. Movellan:
An Alternative to Low-level-Sychrony-Based Methods for Speech Detection. 2029-2037 - Sivan Sabato, Nathan Srebro, Naftali Tishby:
Tight Sample Complexity of Large-Margin Learning. 2038-2046 - Mohammad J. Saberian, Nuno Vasconcelos:
Boosting Classifier Cascades. 2047-2055 - Ruslan Salakhutdinov, Nathan Srebro:
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm. 2056-2064 - Mathieu Salzmann, Raquel Urtasun:
Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation. 2065-2073 - Issei Sato, Kenichi Kurihara, Hiroshi Nakagawa:
Deterministic Single-Pass Algorithm for LDA. 2074-2082 - Christoph Sawade, Niels Landwehr, Tobias Scheffer:
Active Estimation of F-Measures. 2083-2091 - Amin Sayedi, Morteza Zadimoghaddam, Avrim Blum:
Trading off Mistakes and Don't-Know Predictions. 2092-2100 - Katya Scheinberg, Shiqian Ma, Donald Goldfarb:
Sparse Inverse Covariance Selection via Alternating Linearization Methods. 2101-2109 - Joscha Schmiedt, Christian Albers, Klaus Pawelzik:
Spike timing-dependent plasticity as dynamic filter. 2110-2118 - Sohan Seth, Il Memming Park, Austin J. Brockmeier, Mulugeta Semework, John S. Choi, Joseph T. Francis, José Carlos Príncipe:
A novel family of non-parametric cumulative based divergences for point processes. 2119-2127 - Uri Shalit, Daphna Weinshall, Gal Chechik:
Online Learning in The Manifold of Low-Rank Matrices. 2128-2136 - James Sharpnack, Aarti Singh:
Identifying graph-structured activation patterns in networks. 2137-2145 - Pradeep Shenoy, Rajesh P. N. Rao, Angela J. Yu:
A rational decision making framework for inhibitory control. 2146-2154 - Ali Shojaie, George Michailidis:
Penalized Principal Component Regression on Graphs for Analysis of Subnetworks. 2155-2163 - David Silver, Joel Veness:
Monte-Carlo Planning in Large POMDPs. 2164-2172 - Anand Singh, Renaud Jolivet, Pierre J. Magistretti, Bruno Weber:
Sodium entry efficiency during action potentials: A novel single-parameter family of Hodgkin-Huxley models. 2173-2180 - David A. Sontag, Ofer Meshi, Tommi S. Jaakkola, Amir Globerson:
More data means less inference: A pseudo-max approach to structured learning. 2181-2189 - Jonathan Sorg, Satinder Singh, Richard L. Lewis:
Reward Design via Online Gradient Ascent. 2190-2198 - Nathan Srebro, Karthik Sridharan, Ambuj Tewari:
Smoothness, Low Noise and Fast Rates. 2199-2207 - Peter Stobbe, Andreas Krause:
Efficient Minimization of Decomposable Submodular Functions. 2208-2216 - Alexander L. Strehl, John Langford, Lihong Li, Sham M. Kakade:
Learning from Logged Implicit Exploration Data. 2217-2225 - Deqing Sun, Erik B. Sudderth, Michael J. Black:
Layered image motion with explicit occlusions, temporal consistency, and depth ordering. 2226-2234 - Yi Sun, Faustino J. Gomez, Jürgen Schmidhuber:
Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices. 2235-2243 - Umar Syed, Robert E. Schapire:
A Reduction from Apprenticeship Learning to Classification. 2253-2261 - Umar Syed, Ben Taskar:
Semi-Supervised Learning with Adversarially Missing Label Information. 2244-2252 - Zeeshan Syed, John V. Guttag:
Identifying Patients at Risk of Major Adverse Cardiovascular Events Using Symbolic Mismatch. 2262-2270 - Ken Takiyama, Masato Okada:
Switching state space model for simultaneously estimating state transitions and nonstationary firing rates. 2271-2279 - Graham W. Taylor, Rob Fergus, George Williams, Ian Spiro, Christoph Bregler:
Pose-Sensitive Embedding by Nonlinear NCA Regression. 2280-2288 - Bo Thiesson, Chong Wang:
Fast Large-scale Mixture Modeling with Component-specific Data Partitions. 2289-2297 - Emanuel Todorov:
Policy gradients in linearly-solvable MDPs. 2298-2306 - Fabian Triefenbach, Azarakhsh Jalalvand, Benjamin Schrauwen, Jean-Pierre Martens:
Phoneme Recognition with Large Hierarchical Reservoirs. 2307-2315 - Matthew Urry, Peter Sollich:
Exact learning curves for Gaussian process regression on large random graphs. 2316-2324 - Gaël Varoquaux, Alexandre Gramfort, Jean-Baptiste Poline, Bertrand Thirion:
Brain covariance selection: better individual functional connectivity models using population prior. 2334-2342 - Jean-Philippe Vert, Kevin Bleakley:
Fast detection of multiple change-points shared by many signals using group LARS. 2343-2351 - Paolo Viappiani, Craig Boutilier:
Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets. 2352-2360 - Meritxell Vinyals, Jesús Cerquides, Alessandro Farinelli, Juan A. Rodríguez-Aguilar:
Worst-case bounds on the quality of max-product fixed-points. 2325-2333 - S. V. N. Vishwanathan, Zhaonan Sun, Nawanol Ampornpunt, Manik Varma:
Multiple Kernel Learning and the SMO Algorithm. 2361-2369 - Ernesto De Vito, Lorenzo Rosasco, Alessandro Toigo:
Spectral Regularization for Support Estimation. 487-495 - Meihong Wang, Fei Sha, Michael I. Jordan:
Unsupervised Kernel Dimension Reduction. 2379-2387 - Wei Wang, Zhi-Hua Zhou:
Multi-View Active Learning in the Non-Realizable Case. 2388-2396 - Yang Wang, Greg Mori:
A Discriminative Latent Model of Image Region and Object Tag Correspondence. 2397-2405 - Eric Wang, Dehong Liu, Jorge G. Silva, David B. Dunson, Lawrence Carin:
Joint Analysis of Time-Evolving Binary Matrices and Associated Documents. 2370-2378 - Fabian L. Wauthier, Michael I. Jordan:
Heavy-Tailed Process Priors for Selective Shrinkage. 2406-2414 - David J. Weiss, Benjamin Sapp, Ben Taskar:
Sidestepping Intractable Inference with Structured Ensemble Cascades. 2415-2423 - Peter Welinder, Steve Branson, Serge J. Belongie, Pietro Perona:
The Multidimensional Wisdom of Crowds. 2424-2432 - Martha White, Adam White:
Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains. 2433-2441 - Jenna Wiens, John V. Guttag:
Active Learning Applied to Patient-Adaptive Heartbeat Classification. 2442-2450 - Oliver Williams, Frank McSherry:
Probabilistic Inference and Differential Privacy. 2451-2459 - Andrew Gordon Wilson, Zoubin Ghahramani:
Copula Processes. 2460-2468 - Adrien Wohrer, Ranulfo Romo, Christian K. Machens:
Linear readout from a neural population with partial correlation data. 2469-2477 - Yi-Da Wu, Shi-Jie Lin, Hsin Chen:
A Log-Domain Implementation of the Diffusion Network in Very Large Scale Integration. 2487-2495 - Shuang Wu, Xuming He, Hongjing Lu, Alan L. Yuille:
A unified model of short-range and long-range motion perception. 2478-2486 - Huan Xu, Shie Mannor:
Distributionally Robust Markov Decision Processes. 2505-2513 - Yang Xu, Charles Kemp:
Inference and communication in the game of Password. 2514-2522 - Huan Xu, Constantine Caramanis, Sujay Sanghavi:
Robust PCA via Outlier Pursuit. 2496-2504 - Yaoliang Yu, Min Yang, Linli Xu, Martha White, Dale Schuurmans:
Relaxed Clipping: A Global Training Method for Robust Regression and Classification. 2532-2540 - Stella X. Yu:
Feature Transitions with Saccadic Search: Size, Color, and Orientation Are Not Alike. 2523-2531 - Yi Zhang, Jeff G. Schneider:
Learning Multiple Tasks with a Sparse Matrix-Normal Penalty. 2550-2558 - Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Lower Bounds on Rate of Convergence of Cutting Plane Methods. 2541-2549 - Yu Zhang, Dit-Yan Yeung, Qian Xu:
Probabilistic Multi-Task Feature Selection. 2559-2567 - Yu Zhang, Dit-Yan Yeung:
Worst-Case Linear Discriminant Analysis. 2568-2576 - Hongbo Zhou, Qiang Cheng:
Sufficient Conditions for Generating Group Level Sparsity in a Robust Minimax Framework. 2577-2585 - Jun Zhu, Li-Jia Li, Li Fei-Fei, Eric P. Xing:
Large Margin Learning of Upstream Scene Understanding Models. 2586-2594 - Martin Zinkevich, Markus Weimer, Alexander J. Smola, Lihong Li:
Parallelized Stochastic Gradient Descent. 2595-2603
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