NeurIPS 2009

262 papers

$l_1$-Penalized Robust Estimation for a Class of Inverse Problems Arising in Multiview Geometry Arnak Dalalyan, Renaud Keriven
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3D Object Recognition with Deep Belief Nets Vinod Nair, Geoffrey E. Hinton
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A Bayesian Analysis of Dynamics in Free Recall Richard Socher, Samuel Gershman, Per Sederberg, Kenneth Norman, Adler J. Perotte, David M. Blei
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A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation Lan Du, Lu Ren, Lawrence Carin, David B. Dunson
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A Biologically Plausible Model for Rapid Natural Scene Identification Sennay Ghebreab, Steven Scholte, Victor Lamme, Arnold Smeulders
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A Data-Driven Approach to Modeling Choice Vivek Farias, Srikanth Jagabathula, Devavrat Shah
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A Fast, Consistent Kernel Two-Sample Test Arthur Gretton, Kenji Fukumizu, Zaïd Harchaoui, Bharath K. Sriperumbudur
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A Game-Theoretic Approach to Hypergraph Clustering Samuel R. Bulò, Marcello Pelillo
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A Gaussian Tree Approximation for Integer Least-Squares Jacob Goldberger, Amir Leshem
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A General Projection Property for Distribution Families Yao-liang Yu, Yuxi Li, Dale Schuurmans, Csaba Szepesvári
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A Generalized Natural Actor-Critic Algorithm Tetsuro Morimura, Eiji Uchibe, Junichiro Yoshimoto, Kenji Doya
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A Joint Maximum-Entropy Model for Binary Neural Population Patterns and Continuous Signals Sebastian Gerwinn, Philipp Berens, Matthias Bethge
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A Neural Implementation of the Kalman Filter Robert Wilson, Leif Finkel
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A Parameter-Free Hedging Algorithm Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu
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A Rate Distortion Approach for Semi-Supervised Conditional Random Fields Yang Wang, Gholamreza Haffari, Shaojun Wang, Greg Mori
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A Smoothed Approximate Linear Program Vijay Desai, Vivek Farias, Ciamac C. Moallemi
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A Sparse Non-Parametric Approach for Single Channel Separation of Known Sounds Paris Smaragdis, Madhusudana Shashanka, Bhiksha Raj
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A Stochastic Approximation Method for Inference in Probabilistic Graphical Models Peter Carbonetto, Matthew King, Firas Hamze
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A Unified Framework for High-Dimensional Analysis of $m$-Estimators with Decomposable Regularizers Sahand Negahban, Bin Yu, Martin J. Wainwright, Pradeep K. Ravikumar
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Abstraction and Relational Learning Charles Kemp, Alan Jern
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Accelerated Gradient Methods for Stochastic Optimization and Online Learning Chonghai Hu, Weike Pan, James T. Kwok
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Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models Baback Moghaddam, Mohammad Emtiyaz Khan, Kevin P. Murphy, Benjamin M. Marlin
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Adapting to the Shifting Intent of Search Queries Umar Syed, Aleksandrs Slivkins, Nina Mishra
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Adaptive Design Optimization in Experiments with People Daniel Cavagnaro, Jay Myung, Mark A. Pitt
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Adaptive Regularization for Transductive Support Vector Machine Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Lyu, Zhirong Yang
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Adaptive Regularization of Weight Vectors Koby Crammer, Alex Kulesza, Mark Dredze
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An Additive Latent Feature Model for Transparent Object Recognition Mario Fritz, Gary Bradski, Sergey Karayev, Trevor Darrell, Michael J. Black
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An Infinite Factor Model Hierarchy via a Noisy-or Mechanism Douglas Eck, Yoshua Bengio, Aaron C. Courville
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An Integer Projected Fixed Point Method for Graph Matching and MAP Inference Marius Leordeanu, Martial Hebert, Rahul Sukthankar
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An LP View of the M-Best MAP Problem Menachem Fromer, Amir Globerson
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An Online Algorithm for Large Scale Image Similarity Learning Gal Chechik, Uri Shalit, Varun Sharma, Samy Bengio
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Analysis of SVM with Indefinite Kernels Yiming Ying, Colin Campbell, Mark Girolami
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Anomaly Detection with Score Functions Based on Nearest Neighbor Graphs Manqi Zhao, Venkatesh Saligrama
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Approximating MAP by Compensating for Structural Relaxations Arthur Choi, Adnan Darwiche
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Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing Sundeep Rangan, Vivek Goyal, Alyson K. Fletcher
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Asymptotically Optimal Regularization in Smooth Parametric Models Percy Liang, Guillaume Bouchard, Francis R. Bach, Michael I. Jordan
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AUC Optimization and the Two-Sample Problem Nicolas Vayatis, Marine Depecker, Stéphan J. Clémençcon
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Augmenting Feature-Driven fMRI Analyses: Semi-Supervised Learning and Resting State Activity Andreas Bartels, Matthew Blaschko, Jacquelyn A. Shelton
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Bayesian Belief Polarization Alan Jern, Kai-min Chang, Charles Kemp
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Bayesian Estimation of Orientation Preference Maps Sebastian Gerwinn, Leonard White, Matthias Kaschube, Matthias Bethge, Jakob H. Macke
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Bayesian Nonparametric Models on Decomposable Graphs Francois Caron, Arnaud Doucet
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Bayesian Source Localization with the Multivariate Laplace Prior Marcel V. Gerven, Botond Cseke, Robert Oostenveld, Tom Heskes
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Bayesian Sparse Factor Models and DAGs Inference and Comparison Ricardo Henao, Ole Winther
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Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships Tomasz Malisiewicz, Alyosha Efros
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Beyond Convexity: Online Submodular Minimization Elad Hazan, Satyen Kale
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Bilinear Classifiers for Visual Recognition Hamed Pirsiavash, Deva Ramanan, Charless C. Fowlkes
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Boosting with Spatial Regularization Yongxin Xi, Uri Hasson, Peter J. Ramadge, Zhen J. Xiang
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Bootstrapping from Game Tree Search Joel Veness, David Silver, Alan Blair, William Uther
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Breaking Boundaries Between Induction Time and Diagnosis Time Active Information Acquisition Ashish Kapoor, Eric Horvitz
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Canonical Time Warping for Alignment of Human Behavior Feng Zhou, Fernando Torre
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Clustering Sequence Sets for Motif Discovery Jong K. Kim, Seungjin Choi
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Code-Specific Policy Gradient Rules for Spiking Neurons Henning Sprekeler, Guillaume Hennequin, Wulfram Gerstner
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Complexity of Decentralized Control: Special Cases Martin Allen, Shlomo Zilberstein
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Compositionality of Optimal Control Laws Emanuel Todorov
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Compressed Least-Squares Regression Odalric Maillard, Rémi Munos
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Conditional Neural Fields Jian Peng, Liefeng Bo, Jinbo Xu
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Conditional Random Fields with High-Order Features for Sequence Labeling Nan Ye, Wee S. Lee, Hai L. Chieu, Dan Wu
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Constructing Topological Maps Using Markov Random Fields and Loop-Closure Detection Roy Anati, Kostas Daniilidis
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Construction of Nonparametric Bayesian Models from Parametric Bayes Equations Peter Orbanz
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Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation Hamid R. Maei, Csaba Szepesvári, Shalabh Bhatnagar, Doina Precup, David Silver, Richard S. Sutton
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Convex Relaxation of Mixture Regression with Efficient Algorithms Novi Quadrianto, John Lim, Dale Schuurmans, Tibério S. Caetano
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Correlation Coefficients Are Insufficient for Analyzing Spike Count Dependencies Arno Onken, Steffen Grünewälder, Klaus Obermayer
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Data-Driven Calibration of Linear Estimators with Minimal Penalties Sylvain Arlot, Francis R. Bach
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Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process Chong Wang, David M. Blei
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Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning Anne Hsu, Thomas L. Griffiths
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Directed Regression Yi-hao Kao, Benjamin V. Roy, Xiang Yan
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Dirichlet-Bernoulli Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance Corpora Shuang-hong Yang, Hongyuan Zha, Bao-gang Hu
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Discrete MDL Predicts in Total Variation Marcus Hutter
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Discriminative Network Models of Schizophrenia Irina Rish, Benjamin Thyreau, Bertrand Thirion, Marion Plaze, Marie-laure Paillere-martinot, Catherine Martelli, Jean-luc Martinot, Jean-baptiste Poline, Guillermo A. Cecchi
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Distribution Matching for Transduction Novi Quadrianto, James Petterson, Alex J. Smola
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Distribution-Calibrated Hierarchical Classification Ofer Dekel
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Dual Averaging Method for Regularized Stochastic Learning and Online Optimization Lin Xiao
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DUOL: A Double Updating Approach for Online Learning Peilin Zhao, Steven C. Hoi, Rong Jin
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Efficient and Accurate Lp-Norm Multiple Kernel Learning Marius Kloft, Ulf Brefeld, Pavel Laskov, Klaus-Robert Müller, Alexander Zien, Sören Sonnenburg
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Efficient Bregman Range Search Lawrence Cayton
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Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models Ryan Mcdonald, Mehryar Mohri, Nathan Silberman, Dan Walker, Gideon S. Mann
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Efficient Learning Using Forward-Backward Splitting Yoram Singer, John C. Duchi
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Efficient Match Kernel Between Sets of Features for Visual Recognition Liefeng Bo, Cristian Sminchisescu
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Efficient Moments-Based Permutation Tests Chunxiao Zhou, Huixia J. Wang, Yongmei M. Wang
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Efficient Recovery of Jointly Sparse Vectors Liang Sun, Jun Liu, Jianhui Chen, Jieping Ye
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Ensemble Nystrom Method Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar
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Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification Amarnag Subramanya, Jeff A. Bilmes
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Estimating Image Bases for Visual Image Reconstruction from Human Brain Activity Yusuke Fujiwara, Yoichi Miyawaki, Yukiyasu Kamitani
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Evaluating Multi-Class Learning Strategies in a Generative Hierarchical Framework for Object Detection Sanja Fidler, Marko Boben, Ales Leonardis
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Explaining Human Multiple Object Tracking as Resource-Constrained Approximate Inference in a Dynamic Probabilistic Model Ed Vul, George Alvarez, Joshua B. Tenenbaum, Michael J. Black
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Exploring Functional Connectivities of the Human Brain Using Multivariate Information Analysis Barry Chai, Dirk Walther, Diane Beck, Li Fei-fei
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Exponential Family Graph Matching and Ranking James Petterson, Jin Yu, Julian J. Mcauley, Tibério S. Caetano
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Extending Phase Mechanism to Differential Motion Opponency for Motion Pop-Out Yicong Meng, Bertram E. Shi
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Factor Modeling for Advertisement Targeting Ye Chen, Michael Kapralov, John Canny, Dmitry Y. Pavlov
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FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs Andrew McCallum, Karl Schultz, Sameer Singh
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Fast Graph Laplacian Regularized Kernel Learning via Semidefinite–Quadratic–Linear Programming Xiao-ming Wu, Anthony M. So, Zhenguo Li, Shuo-yen R. Li
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Fast Image Deconvolution Using Hyper-Laplacian Priors Dilip Krishnan, Rob Fergus
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Fast Learning from Non-I.i.d. Observations Ingo Steinwart, Andreas Christmann
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Fast Subtree Kernels on Graphs Nino Shervashidze, Karsten Borgwardt
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Fast, Smooth and Adaptive Regression in Metric Spaces Samory Kpotufe
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Filtering Abstract Senses from Image Search Results Kate Saenko, Trevor Darrell
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fMRI-Based Inter-Subject Cortical Alignment Using Functional Connectivity Bryan Conroy, Ben Singer, James Haxby, Peter J. Ramadge
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Free Energy Score Space Alessandro Perina, Marco Cristani, Umberto Castellani, Vittorio Murino, Nebojsa Jojic
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From PAC-Bayes Bounds to KL Regularization Pascal Germain, Alexandre Lacasse, Mario Marchand, Sara Shanian, François Laviolette
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Functional Network Reorganization in Motor Cortex Can Be Explained by Reward-Modulated Hebbian Learning Steven Chase, Andrew Schwartz, Wolfgang Maass, Robert A. Legenstein
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Gaussian Process Regression with Student-T Likelihood Jarno Vanhatalo, Pasi Jylänki, Aki Vehtari
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Generalization Errors and Learning Curves for Regression with Multi-Task Gaussian Processes Kian M. Chai
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Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation Yusuke Watanabe, Kenji Fukumizu
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Graph-Based Consensus Maximization Among Multiple Supervised and Unsupervised Models Jing Gao, Feng Liang, Wei Fan, Yizhou Sun, Jiawei Han
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Group Sparse Coding Samy Bengio, Fernando Pereira, Yoram Singer, Dennis Strelow
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Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction Grzegorz Swirszcz, Naoki Abe, Aurelie C. Lozano
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Heavy-Tailed Symmetric Stochastic Neighbor Embedding Zhirong Yang, Irwin King, Zenglin Xu, Erkki Oja
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Help or Hinder: Bayesian Models of Social Goal Inference Tomer Ullman, Chris Baker, Owen Macindoe, Owain Evans, Noah Goodman, Joshua B. Tenenbaum
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Heterogeneous Multitask Learning with Joint Sparsity Constraints Xiaolin Yang, Seyoung Kim, Eric P. Xing
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Hierarchical Learning of Dimensional Biases in Human Categorization Adam Sanborn, Nick Chater, Katherine A. Heller
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Hierarchical Mixture of Classification Experts Uncovers Interactions Between Brain Regions Bangpeng Yao, Dirk Walther, Diane Beck, Li Fei-fei
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Hierarchical Modeling of Local Image Features Through $L_p$-Nested Symmetric Distributions Matthias Bethge, Eero P. Simoncelli, Fabian H. Sinz
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Human Rademacher Complexity Xiaojin Zhu, Bryan R. Gibson, Timothy T. Rogers
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Improving Existing Fault Recovery Policies Guy Shani, Christopher Meek
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Indian Buffet Processes with Power-Law Behavior Yee W. Teh, Dilan Gorur
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Individuation, Identification and Object Discovery Charles Kemp, Alan Jern, Fei Xu
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Information-Theoretic Lower Bounds on the Oracle Complexity of Convex Optimization Alekh Agarwal, Martin J. Wainwright, Peter L. Bartlett, Pradeep K. Ravikumar
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Inter-Domain Gaussian Processes for Sparse Inference Using Inducing Features Miguel Lázaro-Gredilla, Aníbal Figueiras-Vidal
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Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions Kenji Fukumizu, Arthur Gretton, Gert R. Lanckriet, Bernhard Schölkopf, Bharath K. Sriperumbudur
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Kernel Methods for Deep Learning Youngmin Cho, Lawrence K. Saul
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Kernels and Learning Curves for Gaussian Process Regression on Random Graphs Peter Sollich, Matthew Urry, Camille Coti
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Know Thy Neighbour: A Normative Theory of Synaptic Depression Jean-pascal Pfister, Peter Dayan, Máté Lengyel
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Label Selection on Graphs Andrew Guillory, Jeff A. Bilmes
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Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process Finale Doshi-velez, Shakir Mohamed, Zoubin Ghahramani, David A. Knowles
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Lattice Regression Eric Garcia, Maya Gupta
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Learning a Small Mixture of Trees M. P. Kumar, Daphne Koller
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Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data Shuai Huang, Jing Li, Liang Sun, Jun Liu, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman, Jieping Ye
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Learning Bregman Distance Functions and Its Application for Semi-Supervised Clustering Lei Wu, Rong Jin, Steven C. Hoi, Jianke Zhu, Nenghai Yu
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Learning from Multiple Partially Observed Views - An Application to Multilingual Text Categorization Massih R. Amini, Nicolas Usunier, Cyril Goutte
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Learning from Neighboring Strokes: Combining Appearance and Context for Multi-Domain Sketch Recognition Tom Ouyang, Randall Davis
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Learning in Markov Random Fields Using Tempered Transitions Ruslan Salakhutdinov
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Learning Label Embeddings for Nearest-Neighbor Multi-Class Classification with an Application to Speech Recognition Natasha Singh-miller, Michael Collins
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Learning Models of Object Structure Joseph Schlecht, Kobus Barnard
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Learning Non-Linear Combinations of Kernels Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
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Learning to Explore and Exploit in POMDPs Chenghui Cai, Xuejun Liao, Lawrence Carin
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Learning to Hash with Binary Reconstructive Embeddings Brian Kulis, Trevor Darrell
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Learning to Rank by Optimizing NDCG Measure Hamed Valizadegan, Rong Jin, Ruofei Zhang, Jianchang Mao
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Learning Transport Operators for Image Manifolds Benjamin Culpepper, Bruno A. Olshausen
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Learning with Compressible Priors Volkan Cevher
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Linear-Time Algorithms for Pairwise Statistical Problems Parikshit Ram, Dongryeol Lee, William March, Alexander G. Gray
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Linearly Constrained Bayesian Matrix Factorization for Blind Source Separation Mikkel Schmidt
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Local Rules for Global MAP: When Do They Work ? Kyomin Jung, Pushmeet Kohli, Devavrat Shah
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Locality-Sensitive Binary Codes from Shift-Invariant Kernels Maxim Raginsky, Svetlana Lazebnik
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Localizing Bugs in Program Executions with Graphical Models Laura Dietz, Valentin Dallmeier, Andreas Zeller, Tobias Scheffer
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Lower Bounds on Minimax Rates for Nonparametric Regression with Additive Sparsity and Smoothness Garvesh Raskutti, Bin Yu, Martin J. Wainwright
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Manifold Embeddings for Model-Based Reinforcement Learning Under Partial Observability Keith Bush, Joelle Pineau
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Manifold Regularization for SIR with Rate Root-N Convergence Wei Bian, Dacheng Tao
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Matrix Completion from Noisy Entries Raghunandan Keshavan, Andrea Montanari, Sewoong Oh
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Matrix Completion from Power-Law Distributed Samples Raghu Meka, Prateek Jain, Inderjit S. Dhillon
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Maximin Affinity Learning of Image Segmentation Kevin Briggman, Winfried Denk, Sebastian Seung, Moritz N. Helmstaedter, Srinivas C. Turaga
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Maximum Likelihood Trajectories for Continuous-Time Markov Chains Theodore J. Perkins
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Measuring Invariances in Deep Networks Ian Goodfellow, Honglak Lee, Quoc V. Le, Andrew Saxe, Andrew Y. Ng
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Measuring Model Complexity with the Prior Predictive Wolf Vanpaemel
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Modeling Social Annotation Data with Content Relevance Using a Topic Model Tomoharu Iwata, Takeshi Yamada, Naonori Ueda
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Modeling the Spacing Effect in Sequential Category Learning Hongjing Lu, Matthew Weiden, Alan L. Yuille
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Modelling Relational Data Using Bayesian Clustered Tensor Factorization Ilya Sutskever, Joshua B. Tenenbaum, Ruslan Salakhutdinov
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Monte Carlo Sampling for Regret Minimization in Extensive Games Marc Lanctot, Kevin Waugh, Martin Zinkevich, Michael Bowling
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Multi-Label Prediction via Compressed Sensing Daniel J. Hsu, Sham M. Kakade, John Langford, Tong Zhang
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Multi-Label Prediction via Sparse Infinite CCA Piyush Rai, Hal Daume
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Multi-Step Dyna Planning for Policy Evaluation and Control Hengshuai Yao, Shalabh Bhatnagar, Dongcui Diao, Richard S. Sutton, Csaba Szepesvári
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Multiple Incremental Decremental Learning of Support Vector Machines Masayuki Karasuyama, Ichiro Takeuchi
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Nash Equilibria of Static Prediction Games Michael Brückner, Tobias Scheffer
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Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling Lei Shi, Thomas L. Griffiths
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Neurometric Function Analysis of Population Codes Philipp Berens, Sebastian Gerwinn, Alexander Ecker, Matthias Bethge
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No Evidence for Active Sparsification in the Visual Cortex Pietro Berkes, Ben White, Jozsef Fiser
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Noise Characterization, Modeling, and Reduction for in Vivo Neural Recording Zhi Yang, Qi Zhao, Edward Keefer, Wentai Liu
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Noisy Generalized Binary Search Robert Nowak
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Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations Mingyuan Zhou, Haojun Chen, Lu Ren, Guillermo Sapiro, Lawrence Carin, John W. Paisley
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Non-Stationary Continuous Dynamic Bayesian Networks Marco Grzegorczyk, Dirk Husmeier
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Nonlinear Directed Acyclic Structure Learning with Weakly Additive Noise Models Arthur Gretton, Peter Spirtes, Robert E. Tillman
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Nonlinear Learning Using Local Coordinate Coding Kai Yu, Tong Zhang, Yihong Gong
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Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution Cosmin Bejan, Matthew Titsworth, Andrew Hickl, Sanda Harabagiu
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Nonparametric Bayesian Texture Learning and Synthesis Long Zhu, Yuanahao Chen, Bill Freeman, Antonio Torralba
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Nonparametric Greedy Algorithms for the Sparse Learning Problem Han Liu, Xi Chen
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Nonparametric Latent Feature Models for Link Prediction Kurt Miller, Michael I. Jordan, Thomas L. Griffiths
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Occlusive Components Analysis Jörg Lücke, Richard Turner, Maneesh Sahani, Marc Henniges
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On Invariance in Hierarchical Models Jake Bouvrie, Lorenzo Rosasco, Tomaso Poggio
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On Learning Rotations Raman Arora
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On Stochastic and Worst-Case Models for Investing Elad Hazan, Satyen Kale
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On the Algorithmics and Applications of a Mixed-Norm Based Kernel Learning Formulation Saketha N. Jagarlapudi, Dinesh G, Raman S, Chiranjib Bhattacharyya, Aharon Ben-tal, Ramakrishnan K.r.
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On the Convergence of the Concave-Convex Procedure Gert R. Lanckriet, Bharath K. Sriperumbudur
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Online Learning of Assignments Matthew Streeter, Daniel Golovin, Andreas Krause
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Optimal Context Separation of Spiking Haptic Signals by Second-Order Somatosensory Neurons Romain Brasselet, Roland Johansson, Angelo Arleo
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Optimal Scoring for Unsupervised Learning Zhihua Zhang, Guang Dai
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Optimizing Multi-Class Spatio-Spectral Filters via Bayes Error Estimation for EEG Classification Wenming Zheng, Zhouchen Lin
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Orthogonal Matching Pursuit from Noisy Random Measurements: A New Analysis Sundeep Rangan, Alyson K. Fletcher
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Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units Feng Yan, Ningyi Xu, Yuan Qi
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Particle-Based Variational Inference for Continuous Systems Andrew Frank, Padhraic Smyth, Alexander T. Ihler
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Perceptual Multistability as Markov Chain Monte Carlo Inference Samuel Gershman, Ed Vul, Joshua B. Tenenbaum
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Periodic Step Size Adaptation for Single Pass On-Line Learning Chun-nan Hsu, Yu-ming Chang, Hanshen Huang, Yuh-jye Lee
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Polynomial Semantic Indexing Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Kunihiko Sadamasa, Yanjun Qi, Corinna Cortes, Mehryar Mohri
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Positive Semidefinite Metric Learning with Boosting Chunhua Shen, Junae Kim, Lei Wang, Anton Hengel
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Posterior vs Parameter Sparsity in Latent Variable Models Kuzman Ganchev, Ben Taskar, Fernando Pereira, João Gama
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Potential-Based Agnostic Boosting Varun Kanade, Adam Kalai
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Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory Harold Pashler, Nicholas Cepeda, Robert Lindsey, Ed Vul, Michael Mozer
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Probabilistic Relational PCA Wu-jun Li, Dit-Yan Yeung, Zhihua Zhang
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Quantification and the Language of Thought Charles Kemp
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Randomized Pruning: Efficiently Calculating Expectations in Large Dynamic Programs Alexandre Bouchard-côté, Slav Petrov, Dan Klein
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Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions Parikshit Ram, Dongryeol Lee, Hua Ouyang, Alexander G. Gray
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Ranking Measures and Loss Functions in Learning to Rank Wei Chen, Tie-yan Liu, Yanyan Lan, Zhi-ming Ma, Hang Li
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Reading Tea Leaves: How Humans Interpret Topic Models Jonathan Chang, Sean Gerrish, Chong Wang, Jordan L. Boyd-graber, David M. Blei
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Reconstruction of Sparse Circuits Using Multi-Neuronal Excitation (RESCUME) Tao Hu, Anthony Leonardo, Dmitri B. Chklovskii
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Region-Based Segmentation and Object Detection Stephen Gould, Tianshi Gao, Daphne Koller
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Regularized Distance Metric Learning:Theory and Algorithm Rong Jin, Shijun Wang, Yang Zhou
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Replacing Supervised Classification Learning by Slow Feature Analysis in Spiking Neural Networks Stefan Klampfl, Wolfgang Maass
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Replicated SoftMax: An Undirected Topic Model Geoffrey E. Hinton, Ruslan Salakhutdinov
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Rethinking LDA: Why Priors Matter Hanna M. Wallach, David M. Mimno, Andrew McCallum
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Riffled Independence for Ranked Data Jonathan Huang, Carlos Guestrin
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Robust Nonparametric Regression with Metric-Space Valued Output Matthias Hein
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Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization John Wright, Arvind Ganesh, Shankar Rao, Yigang Peng, Yi Ma
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Robust Value Function Approximation Using Bilinear Programming Marek Petrik, Shlomo Zilberstein
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Segmenting Scenes by Matching Image Composites Bryan Russell, Alyosha Efros, Josef Sivic, Bill Freeman, Andrew Zisserman
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Semi-Supervised Learning in Gigantic Image Collections Rob Fergus, Yair Weiss, Antonio Torralba
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Semi-Supervised Learning Using Sparse Eigenfunction Bases Kaushik Sinha, Mikhail Belkin
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Semi-Supervised Regression Using Hessian Energy with an Application to Semi-Supervised Dimensionality Reduction Kwang I. Kim, Florian Steinke, Matthias Hein
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Sensitivity Analysis in HMMs with Application to Likelihood Maximization Pierre-arnaud Coquelin, Romain Deguest, Rémi Munos
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Sequential Effects Reflect Parallel Learning of Multiple Environmental Regularities Matthew Wilder, Matt Jones, Michael Mozer
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Sharing Features Among Dynamical Systems with Beta Processes Emily B. Fox, Michael I. Jordan, Erik B. Sudderth, Alan S. Willsky
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Skill Discovery in Continuous Reinforcement Learning Domains Using Skill Chaining George Konidaris, Andrew G. Barto
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Slow Learners Are Fast Martin Zinkevich, John Langford, Alex J. Smola
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Slow, Decorrelated Features for Pretraining Complex Cell-like Networks Yoshua Bengio, James S. Bergstra
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Solving Stochastic Games Liam M. Dermed, Charles L. Isbell
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Sparse and Locally Constant Gaussian Graphical Models Jean Honorio, Dimitris Samaras, Nikos Paragios, Rita Goldstein, Luis E. Ortiz
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Sparse Estimation Using General Likelihoods and Non-Factorial Priors David P. Wipf, Srikantan S. Nagarajan
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Sparse Metric Learning via Smooth Optimization Yiming Ying, Kaizhu Huang, Colin Campbell
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Sparsistent Learning of Varying-Coefficient Models with Structural Changes Mladen Kolar, Le Song, Eric P. Xing
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Spatial Normalized Gamma Processes Vinayak Rao, Yee W. Teh
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Speaker Comparison with Inner Product Discriminant Functions Zahi Karam, Douglas Sturim, William M. Campbell
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Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing Matthias Seeger
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Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data Boaz Nadler, Nathan Srebro, Xueyuan Zhou
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Statistical Consistency of Top-K Ranking Fen Xia, Tie-yan Liu, Hang Li
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Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing Ruben Coen-cagli, Peter Dayan, Odelia Schwartz
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STDP Enables Spiking Neurons to Detect Hidden Causes of Their Inputs Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass
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Strategy Grafting in Extensive Games Kevin Waugh, Nolan Bard, Michael Bowling
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Streaming K-Means Approximation Nir Ailon, Ragesh Jaiswal, Claire Monteleoni
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Streaming Pointwise Mutual Information Benjamin V. Durme, Ashwin Lall
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Structural Inference Affects Depth Perception in the Context of Potential Occlusion Ian Stevenson, Konrad Koerding
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Structured Output Regression for Detection with Partial Truncation Andrea Vedaldi, Andrew Zisserman
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Subject Independent EEG-Based BCI Decoding Siamac Fazli, Cristian Grozea, Marton Danoczy, Benjamin Blankertz, Florin Popescu, Klaus-Robert Müller
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Submanifold Density Estimation Arkadas Ozakin, Alexander G. Gray
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Submodularity Cuts and Applications Yoshinobu Kawahara, Kiyohito Nagano, Koji Tsuda, Jeff A. Bilmes
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Sufficient Conditions for Agnostic Active Learnable Liwei Wang
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The 'tree-Dependent Components' of Natural Scenes Are Edge Filters Daniel Zoran, Yair Weiss
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The Infinite Partially Observable Markov Decision Process Finale Doshi-velez
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The Ordered Residual Kernel for Robust Motion Subspace Clustering Tat-jun Chin, Hanzi Wang, David Suter
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The Wisdom of Crowds in the Recollection of Order Information Mark Steyvers, Brent Miller, Pernille Hemmer, Michael D. Lee
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Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation Shuheng Zhou
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Time-Rescaling Methods for the Estimation and Assessment of Non-Poisson Neural Encoding Models Jonathan W. Pillow
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Time-Varying Dynamic Bayesian Networks Le Song, Mladen Kolar, Eric P. Xing
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Toward Provably Correct Feature Selection in Arbitrary Domains Dimitris Margaritis
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