NeurIPS 2011

306 papers

$\theta$-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene Understanding Congcong Li, Ashutosh Saxena, Tsuhan Chen
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A Blind Sparse Deconvolution Method for Neural Spike Identification Chaitanya Ekanadham, Daniel Tranchina, Eero P. Simoncelli
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A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm Julie Dethier, Paul Nuyujukian, Chris Eliasmith, Terrence C. Stewart, Shauki A. Elasaad, Krishna V. Shenoy, Kwabena A. Boahen
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A Collaborative Mechanism for Crowdsourcing Prediction Problems Jacob D. Abernethy, Rafael M. Frongillo
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A Concave Regularization Technique for Sparse Mixture Models Martin O. Larsson, Johan Ugander
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A Convergence Analysis of Log-Linear Training Simon Wiesler, Hermann Ney
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A Denoising View of Matrix Completion Weiran Wang, Miguel Á. Carreira-Perpiñán, Zhengdong Lu
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A Global Structural EM Algorithm for a Model of Cancer Progression Ali Tofigh, Erik Sj̦lund, Mattias H̦glund, Jens Lagergren
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A Machine Learning Approach to Predict Chemical Reactions Matthew A. Kayala, Pierre F. Baldi
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A Model for Temporal Dependencies in Event Streams Asela Gunawardana, Christopher Meek, Puyang Xu
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A More Powerful Two-Sample Test in High Dimensions Using Random Projection Miles Lopes, Laurent Jacob, Martin J. Wainwright
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A Non-Parametric Approach to Dynamic Programming Oliver B. Kroemer, Jan R. Peters
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A Rational Model of Causal Inference with Continuous Causes Thomas L. Griffiths, Michael James
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A Reinforcement Learning Theory for Homeostatic Regulation Mehdi Keramati, Boris S. Gutkin
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A Reinterpretation of the Policy Oscillation Phenomenon in Approximate Policy Iteration Paul Wagner
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A Two-Stage Weighting Framework for Multi-Source Domain Adaptation Qian Sun, Rita Chattopadhyay, Sethuraman Panchanathan, Jieping Ye
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Accelerated Adaptive Markov Chain for Partition Function Computation Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
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Action-Gap Phenomenon in Reinforcement Learning Amir-massoud Farahmand
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Active Classification Based on Value of Classifier Tianshi Gao, Daphne Koller
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Active Dendrites: Adaptation to Spike-Based Communication Balazs B. Ujfalussy, Máté Lengyel
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Active Learning of Neural Response Functions with Gaussian Processes Mijung Park, Greg Horwitz, Jonathan W. Pillow
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Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity Nir Ailon
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Active Learning with a Drifting Distribution Liu Yang
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Active Ranking Using Pairwise Comparisons Kevin G. Jamieson, Robert Nowak
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Adaptive Hedge Tim V. Erven, Wouter M. Koolen, Steven D. Rooij, Peter Grünwald
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Additive Gaussian Processes David K. Duvenaud, Hannes Nickisch, Carl E. Rasmussen
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Advice Refinement in Knowledge-Based SVMs Gautam Kunapuli, Richard Maclin, Jude W. Shavlik
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Agnostic Selective Classification Yair Wiener, Ran El-Yaniv
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Algorithms and Hardness Results for Parallel Large Margin Learning Phil Long, Rocco Servedio
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Algorithms for Hyper-Parameter Optimization James S. Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl
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An Application of Tree-Structured Expectation Propagation for Channel Decoding Pablo M. Olmos, Luis Salamanca, Juan Fuentes, Fernando Pérez-Cruz
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An Empirical Evaluation of Thompson Sampling Olivier Chapelle, Lihong Li
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An Exact Algorithm for F-Measure Maximization Krzysztof J. Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier
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An Ideal Observer Model for Identifying the Reference Frame of Objects Joseph L. Austerweil, Abram L. Friesen, Thomas L. Griffiths
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An Unsupervised Decontamination Procedure for Improving the Reliability of Human Judgments Michael Mozer, Benjamin Link, Harold Pashler
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Analysis and Improvement of Policy Gradient Estimation Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama
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Analytical Results for the Error in Filtering of Gaussian Processes Alex K. Susemihl, Ron Meir, Manfred Opper
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Anatomically Constrained Decoding of Finger Flexion from Electrocorticographic Signals Zuoguan Wang, Gerwin Schalk, Qiang Ji
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Approximating Semidefinite Programs in Sublinear Time Dan Garber, Elad Hazan
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Automated Refinement of Bayes Networks' Parameters Based on Test Ordering Constraints Omar Z. Khan, Pascal Poupart, John-mark M. Agosta
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Autonomous Learning of Action Models for Planning Neville Mehta, Prasad Tadepalli, Alan Fern
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Bayesian Bias Mitigation for Crowdsourcing Fabian L. Wauthier, Michael I. Jordan
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Bayesian Partitioning of Large-Scale Distance Data David Adametz, Volker Roth
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Bayesian Spike-Triggered Covariance Analysis ll Memming Park, Jonathan W. Pillow
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Beating SGD: Learning SVMs in Sublinear Time Elad Hazan, Tomer Koren, Nati Srebro
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Better Mini-Batch Algorithms via Accelerated Gradient Methods Andrew Cotter, Ohad Shamir, Nati Srebro, Karthik Sridharan
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Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts Matthias Hein, Simon Setzer
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Blending Autonomous Exploration and Apprenticeship Learning Thomas J. Walsh, Daniel K. Hewlett, Clayton T. Morrison
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Boosting with Maximum Adaptive Sampling Charles Dubout, Francois Fleuret
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Budgeted Optimization with Concurrent Stochastic-Duration Experiments Javad Azimi, Alan Fern, Xiaoli Z. Fern
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Clustered Multi-Task Learning via Alternating Structure Optimization Jiayu Zhou, Jianhui Chen, Jieping Ye
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Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery Scott Niekum, Andrew G. Barto
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Co-Regularized Multi-View Spectral Clustering Abhishek Kumar, Piyush Rai, Hal Daume
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Co-Training for Domain Adaptation Minmin Chen, Kilian Q. Weinberger, John Blitzer
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Collective Graphical Models Daniel R. Sheldon, Thomas G. Dietterich
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Committing Bandits Loc X. Bui, Ramesh Johari, Shie Mannor
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Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs Armen Allahverdyan, Aram Galstyan
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Complexity of Inference in Latent Dirichlet Allocation David Sontag, Dan Roy
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Composite Multiclass Losses Elodie Vernet, Mark D. Reid, Robert C. Williamson
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Confidence Sets for Network Structure David S. Choi, Patrick J. Wolfe, Edoardo M. Airoldi
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Contextual Gaussian Process Bandit Optimization Andreas Krause, Cheng S. Ong
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Continuous-Time Regression Models for Longitudinal Networks Duy Q. Vu, David Hunter, Padhraic Smyth, Arthur U. Asuncion
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Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization Mark Schmidt, Nicolas L. Roux, Francis R. Bach
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Convergent Bounds on the Euclidean Distance Yoonho Hwang, Hee-kap Ahn
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Convergent Fitted Value Iteration with Linear Function Approximation Daniel J. Lizotte
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Crowdclustering Ryan G. Gomes, Peter Welinder, Andreas Krause, Pietro Perona
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Data Skeletonization via Reeb Graphs Xiaoyin Ge, Issam I. Safa, Mikhail Belkin, Yusu Wang
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Demixed Principal Component Analysis Wieland Brendel, Ranulfo Romo, Christian K. Machens
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Differentially Private M-Estimators Jing Lei
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Dimensionality Reduction Using the Sparse Linear Model Ioannis A. Gkioulekas, Todd Zickler
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Directed Graph Embedding: An Algorithm Based on Continuous Limits of Laplacian-Type Operators Dominique C. Perrault-joncas, Marina Meila
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Distributed Delayed Stochastic Optimization Alekh Agarwal, John C. Duchi
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Divide-and-Conquer Matrix Factorization Lester W. Mackey, Michael I. Jordan, Ameet Talwalkar
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Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection Richard Socher, Eric H. Huang, Jeffrey Pennin, Christopher D. Manning, Andrew Y. Ng
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Dynamical Segmentation of Single Trials from Population Neural Data Biljana Petreska, Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
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Efficient Anomaly Detection Using Bipartite k-NN Graphs Kumar Sricharan, Alfred O. Hero
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Efficient Coding of Natural Images with a Population of Noisy Linear-Nonlinear Neurons Yan Karklin, Eero P. Simoncelli
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Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials Philipp Krähenbühl, Vladlen Koltun
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Efficient Inference in Matrix-Variate Gaussian Models with \iid Observation Noise Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten Borgwardt
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Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression Sham M. Kakade, Varun Kanade, Ohad Shamir, Adam Kalai
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Efficient Methods for Overlapping Group Lasso Lei Yuan, Jun Liu, Jieping Ye
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Efficient Offline Communication Policies for Factored Multiagent POMDPs João V. Messias, Matthijs Spaan, Pedro U. Lima
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Efficient Online Learning via Randomized Rounding Nicolò Cesa-bianchi, Ohad Shamir
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EigenNet: A Bayesian Hybrid of Generative and Conditional Models for Sparse Learning Feng Yan, Yuan Qi
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Emergence of Multiplication in a Biophysical Model of a Wide-Field Visual Neuron for Computing Object Approaches: Dynamics, Peaks, & Fits Matthias S. Keil
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Empirical Models of Spiking in Neural Populations Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
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Energetically Optimal Action Potentials Martin B. Stemmler, Biswa Sengupta, Simon Laughlin, Jeremy Niven
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Environmental Statistics and the Trade-Off Between Model-Based and TD Learning in Humans Dylan A. Simon, Nathaniel D. Daw
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Estimating Time-Varying Input Signals and Ion Channel States from a Single Voltage Trace of a Neuron Ryota Kobayashi, Yasuhiro Tsubo, Petr Lansky, Shigeru Shinomoto
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Evaluating the Inverse Decision-Making Approach to Preference Learning Alan Jern, Christopher G. Lucas, Charles Kemp
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Exploiting Spatial Overlap to Efficiently Compute Appearance Distances Between Image Windows Bogdan Alexe, Viviana Petrescu, Vittorio Ferrari
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Expressive Power and Approximation Errors of Restricted Boltzmann Machines Guido F. Montufar, Johannes Rauh, Nihat Ay
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Extracting Speaker-Specific Information with a Regularized Siamese Deep Network Ke Chen, Ahmad Salman
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Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines Matthew D. Zeiler, Graham W. Taylor, Leonid Sigal, Iain Matthews, Rob Fergus
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Fast and Accurate K-Means for Large Datasets Michael Shindler, Alex Wong, Adam W. Meyerson
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Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition Jia Deng, Sanjeev Satheesh, Alexander C. Berg, Fei Li
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Finite Time Analysis of Stratified Sampling for Monte Carlo Alexandra Carpentier, Rémi Munos
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From Bandits to Experts: On the Value of Side-Observations Shie Mannor, Ohad Shamir
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From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models Skander Mensi, Richard Naud, Wulfram Gerstner
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Gaussian Process Modulated Renewal Processes Yee W. Teh, Vinayak Rao
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Gaussian Process Training with Input Noise Andrew Mchutchon, Carl E. Rasmussen
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Generalised Coupled Tensor Factorisation Kenan Y. Yılmaz, Ali T. Cemgil, Umut Simsekli
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Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss Joseph Keshet, David A. McAllester
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Generalized Beta Mixtures of Gaussians Artin Armagan, Merlise Clyde, David B. Dunson
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Generalized Lasso Based Approximation of Sparse Coding for Visual Recognition Nobuyuki Morioka, Shin'ichi Satoh
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Generalizing from Several Related Classification Tasks to a New Unlabeled Sample Gilles Blanchard, Gyemin Lee, Clayton Scott
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Global Solution of Fully-Observed Variational Bayesian Matrix Factorization Is Column-Wise Independent Shinichi Nakajima, Masashi Sugiyama, S. D. Babacan
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Greedy Algorithms for Structurally Constrained High Dimensional Problems Ambuj Tewari, Pradeep K. Ravikumar, Inderjit S. Dhillon
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Greedy Model Averaging Dong Dai, Tong Zhang
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Group Anomaly Detection Using Flexible Genre Models Liang Xiong, Barnabás Póczos, Jeff G. Schneider
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Hashing Algorithms for Large-Scale Learning Ping Li, Anshumali Shrivastava, Joshua L. Moore, Arnd C. König
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Heavy-Tailed Distances for Gradient Based Image Descriptors Yangqing Jia, Trevor Darrell
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Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms Liefeng Bo, Xiaofeng Ren, Dieter Fox
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Hierarchical Multitask Structured Output Learning for Large-Scale Sequence Segmentation Nico Goernitz, Christian K. Widmer, Georg Zeller, Andre Kahles, Gunnar Rätsch, Sören Sonnenburg
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Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices Xianxing Zhang, Lawrence Carin, David B. Dunson
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Hierarchically Supervised Latent Dirichlet Allocation Adler J. Perotte, Frank Wood, Noemie Elhadad, Nicholas Bartlett
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High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions Animashree Anandkumar, Vincent Tan, Alan S. Willsky
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High-Dimensional Regression with Noisy and Missing Data: Provable Guarantees with Non-Convexity Po-ling Loh, Martin J. Wainwright
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Higher-Order Correlation Clustering for Image Segmentation Sungwoong Kim, Sebastian Nowozin, Pushmeet Kohli, Chang D. Yoo
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History Distribution Matching Method for Predicting Effectiveness of HIV Combination Therapies Jasmina Bogojeska
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Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent Benjamin Recht, Christopher Re, Stephen Wright, Feng Niu
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How Biased Are Maximum Entropy Models? Jakob H. Macke, Iain Murray, Peter E. Latham
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How Do Humans Teach: On Curriculum Learning and Teaching Dimension Faisal Khan, Bilge Mutlu, Xiaojin Zhu
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ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning Quoc V. Le, Alexandre Karpenko, Jiquan Ngiam, Andrew Y. Ng
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Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data Using Sparse Composite Linear Discrimination Analysis Shuai Huang, Jing Li, Jieping Ye, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman
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Im2Text: Describing Images Using 1 Million Captioned Photographs Vicente Ordonez, Girish Kulkarni, Tamara L. Berg
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Image Parsing with Stochastic Scene Grammar Yibiao Zhao, Song-chun Zhu
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Improved Algorithms for Linear Stochastic Bandits Yasin Abbasi-yadkori, Dávid Pál, Csaba Szepesvári
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Improving Topic Coherence with Regularized Topic Models David Newman, Edwin V. Bonilla, Wray Buntine
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Inductive Reasoning About Chimeric Creatures Charles Kemp
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Inference in Continuous-Time Change-Point Models Florian Stimberg, Manfred Opper, Guido Sanguinetti, Andreas Ruttor
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Inferring Interaction Networks Using the IBP Applied to microRNA Target Prediction Hai-son P. Le, Ziv Bar-joseph
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Inferring Spike-Timing-Dependent Plasticity from Spike Train Data Ian Stevenson, Konrad Koerding
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Infinite Latent SVM for Classification and Multi-Task Learning Jun Zhu, Ning Chen, Eric P. Xing
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Information Rates and Optimal Decoding in Large Neural Populations Kamiar R. Rad, Liam Paninski
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Inverting Grice's Maxims to Learn Rules from Natural Language Extractions Mohammad S. Sorower, Janardhan R. Doppa, Walker Orr, Prasad Tadepalli, Thomas G. Dietterich, Xiaoli Z. Fern
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Iterative Learning for Reliable Crowdsourcing Systems David R. Karger, Sewoong Oh, Devavrat Shah
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Joint 3D Estimation of Objects and Scene Layout Andreas Geiger, Christian Wojek, Raquel Urtasun
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k-NN Regression Adapts to Local Intrinsic Dimension Samory Kpotufe
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Kernel Bayes' Rule Kenji Fukumizu, Le Song, Arthur Gretton
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Kernel Embeddings of Latent Tree Graphical Models Le Song, Eric P. Xing, Ankur P. Parikh
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Large-Scale Category Structure Aware Image Categorization Bin Zhao, Fei Li, Eric P. Xing
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Large-Scale Sparse Principal Component Analysis with Application to Text Data Youwei Zhang, Laurent E. Ghaoui
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Learning a Distance Metric from a Network Blake Shaw, Bert Huang, Tony Jebara
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Learning a Tree of Metrics with Disjoint Visual Features Kristen Grauman, Fei Sha, Sung Ju Hwang
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Learning Anchor Planes for Classification Ziming Zhang, Lubor Ladicky, Philip Torr, Amir Saffari
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Learning Auto-Regressive Models from Sequence and Non-Sequence Data Tzu-kuo Huang, Jeff G. Schneider
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Learning Eigenvectors for Free Wouter M. Koolen, Wojciech Kotlowski, Manfred K. Warmuth
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Learning Higher-Order Graph Structure with Features by Structure Penalty Shilin Ding, Grace Wahba, Xiaojin Zhu
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Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint Kenji Fukumizu, Gert R. Lanckriet, Bharath K. Sriperumbudur
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Learning Large-Margin Halfspaces with More Malicious Noise Phil Long, Rocco Servedio
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Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors Chun-Nam Yu, Russell Greiner, Hsiu-Chin Lin, Vickie Baracos
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Learning Person-Object Interactions for Action Recognition in Still Images Vincent Delaitre, Josef Sivic, Ivan Laptev
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Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities Angela Yao, Juergen Gall, Luc V. Gool, Raquel Urtasun
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Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries Zhen J. Xiang, Hao Xu, Peter J. Ramadge
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Learning to Agglomerate Superpixel Hierarchies Viren Jain, Srinivas C. Turaga, K Briggman, Moritz N. Helmstaedter, Winfried Denk, H. S. Seung
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Learning to Learn with Compound HD Models Antonio Torralba, Joshua B. Tenenbaum, Ruslan Salakhutdinov
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Learning to Search Efficiently in High Dimensions Zhen Li, Huazhong Ning, Liangliang Cao, Tong Zhang, Yihong Gong, Thomas S. Huang
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Learning Unbelievable Probabilities Zachary Pitkow, Yashar Ahmadian, Ken D. Miller
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Learning with the Weighted Trace-Norm Under Arbitrary Sampling Distributions Rina Foygel, Ohad Shamir, Nati Srebro, Ruslan Salakhutdinov
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Linear Submodular Bandits and Their Application to Diversified Retrieval Yisong Yue, Carlos Guestrin
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Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation Zhouchen Lin, Risheng Liu, Zhixun Su
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Lower Bounds for Passive and Active Learning Maxim Raginsky, Alexander Rakhlin
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Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds Nitesh Shroff, Pavan Turaga, Rama Chellappa
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MAP Inference for Bayesian Inverse Reinforcement Learning Jaedeug Choi, Kee-eung Kim
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Matrix Completion for Multi-Label Image Classification Ricardo S. Cabral, Fernando Torre, Joao P. Costeira, Alexandre Bernardino
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Maximal Cliques That Satisfy Hard Constraints with Application to Deformable Object Model Learning Xinggang Wang, Xiang Bai, Xingwei Yang, Wenyu Liu, Longin J. Latecki
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Maximum Covariance Unfolding : Manifold Learning for Bimodal Data Vijay Mahadevan, Chi W. Wong, Jose C. Pereira, Tom Liu, Nuno Vasconcelos, Lawrence K. Saul
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Maximum Margin Multi-Instance Learning Hua Wang, Heng Huang, Farhad Kamangar, Feiping Nie, Chris H. Ding
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Maximum Margin Multi-Label Structured Prediction Christoph H. Lampert
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Message-Passing for Approximate MAP Inference with Latent Variables Jiarong Jiang, Piyush Rai, Hal Daume
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Metric Learning with Multiple Kernels Jun Wang, Huyen T. Do, Adam Woznica, Alexandros Kalousis
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Minimax Localization of Structural Information in Large Noisy Matrices Mladen Kolar, Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh
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Modelling Genetic Variations Using Fragmentation-Coagulation Processes Yee W. Teh, Charles Blundell, Lloyd Elliott
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Monte Carlo Value Iteration with Macro-Actions Zhan Lim, Lee Sun, David Hsu
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Multi-Armed Bandits on Implicit Metric Spaces Aleksandrs Slivkins
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Multi-Bandit Best Arm Identification Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric, Sébastien Bubeck
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Multi-View Learning of Word Embeddings via CCA Paramveer Dhillon, Dean P. Foster, Lyle H. Ungar
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Multiclass Boosting: Theory and Algorithms Mohammad J. Saberian, Nuno Vasconcelos
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Multilinear Subspace Regression: An Orthogonal Tensor Decomposition Approach Qibin Zhao, Cesar F. Caiafa, Danilo P. Mandic, Liqing Zhang, Tonio Ball, Andreas Schulze-bonhage, Andrzej S. Cichocki
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Multiple Instance Filtering Kamil A. Wnuk, Stefano Soatto
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Multiple Instance Learning on Structured Data Dan Zhang, Yan Liu, Luo Si, Jian Zhang, Richard D. Lawrence
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Nearest Neighbor Based Greedy Coordinate Descent Inderjit S. Dhillon, Pradeep K. Ravikumar, Ambuj Tewari
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Neural Reconstruction with Approximate Message Passing (NeuRAMP) Alyson K. Fletcher, Sundeep Rangan, Lav R. Varshney, Aniruddha Bhargava
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Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability David P. Reichert, Peggy Series, Amos J. Storkey
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Newtron: An Efficient Bandit Algorithm for Online Multiclass Prediction Elad Hazan, Satyen Kale
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Noise Thresholds for Spectral Clustering Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh
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Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning Eric Moulines, Francis R. Bach
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Non-Conjugate Variational Message Passing for Multinomial and Binary Regression David A. Knowles, Tom Minka
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Non-Parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels Vikas Sindhwani, Aurelie C. Lozano
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Nonlinear Inverse Reinforcement Learning with Gaussian Processes Sergey Levine, Zoran Popovic, Vladlen Koltun
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Nonnegative Dictionary Learning in the Exponential Noise Model for Adaptive Music Signal Representation Onur Dikmen, Cédric Févotte
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Nonstandard Interpretations of Probabilistic Programs for Efficient Inference David Wingate, Noah Goodman, Andreas Stuhlmueller, Jeffrey M. Siskind
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Object Detection with Grammar Models Ross B. Girshick, Pedro F. Felzenszwalb, David A. McAllester
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On Causal Discovery with Cyclic Additive Noise Models Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf
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On Fast Approximate Submodular Minimization Stefanie Jegelka, Hui Lin, Jeff A. Bilmes
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On Learning Discrete Graphical Models Using Greedy Methods Ali Jalali, Christopher C. Johnson, Pradeep K. Ravikumar
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On Strategy Stitching in Large Extensive Form Multiplayer Games Richard G. Gibson, Duane Szafron
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On the Accuracy of L1-Filtering of Signals with Block-Sparse Structure Fatma K. Karzan, Arkadi S. Nemirovski, Boris T. Polyak, Anatoli Juditsky
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On the Analysis of Multi-Channel Neural Spike Data Bo Chen, David E. Carlson, Lawrence Carin
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On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference Guy Broeck
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On the Universality of Online Mirror Descent Nati Srebro, Karthik Sridharan, Ambuj Tewari
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On Tracking the Partition Function Guillaume Desjardins, Yoshua Bengio, Aaron C. Courville
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On U-Processes and Clustering Performance Stéphan J. Clémençcon
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Online Learning: Stochastic, Constrained, and Smoothed Adversaries Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
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Online Submodular Set Cover, Ranking, and Repeated Active Learning Andrew Guillory, Jeff A. Bilmes
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Optimal Learning Rates for Least Squares SVMs Using Gaussian Kernels Mona Eberts, Ingo Steinwart
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Optimal Reinforcement Learning for Gaussian Systems Philipp Hennig
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Optimistic Optimization of a Deterministic Function Without the Knowledge of Its Smoothness Rémi Munos
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Orthogonal Matching Pursuit with Replacement Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon
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PAC-Bayesian Analysis of Contextual Bandits Yevgeny Seldin, Peter Auer, John S. Shawe-taylor, Ronald Ortner, François Laviolette
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Penalty Decomposition Methods for Rank Minimization Yong Zhang, Zhaosong Lu
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Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning Joni K. Pajarinen, Jaakko Peltonen
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Phase Transition in the Family of P-Resistances Morteza Alamgir, Ulrike V. Luxburg
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PiCoDes: Learning a Compact Code for Novel-Category Recognition Alessandro Bergamo, Lorenzo Torresani, Andrew W. Fitzgibbon
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Policy Gradient Coagent Networks Philip S. Thomas
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Portmanteau Vocabularies for Multi-Cue Image Representation Fahad S. Khan, Joost Weijer, Andrew D. Bagdanov, Maria Vanrell
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Practical Variational Inference for Neural Networks Alex Graves
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Predicting Dynamic Difficulty Olana Missura, Thomas Gärtner
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Predicting Response Time and Error Rates in Visual Search Bo Chen, Vidhya Navalpakkam, Pietro Perona
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Prediction Strategies Without Loss Michael Kapralov, Rina Panigrahy
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Priors over Recurrent Continuous Time Processes Ardavan Saeedi, Alexandre Bouchard-côté
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Prismatic Algorithm for Discrete D.C. Programming Problem Yoshinobu Kawahara, Takashi Washio
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Probabilistic Amplitude and Frequency Demodulation Richard Turner, Maneesh Sahani
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Probabilistic Joint Image Segmentation and Labeling Adrian Ion, Joao Carreira, Cristian Sminchisescu
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Probabilistic Modeling of Dependencies Among Visual Short-Term Memory Representations Emin Orhan, Robert A. Jacobs
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Projection onto a Nonnegative Max-Heap Jun Liu, Liang Sun, Jieping Ye
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Pylon Model for Semantic Segmentation Victor Lempitsky, Andrea Vedaldi, Andrew Zisserman
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Quasi-Newton Methods for Markov Chain Monte Carlo Yichuan Zhang, Charles A. Sutton
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Query-Aware MCMC Michael L. Wick, Andrew McCallum
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Randomized Algorithms for Comparison-Based Search Dominique Tschopp, Suhas Diggavi, Payam Delgosha, Soheil Mohajer
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Ranking Annotators for Crowdsourced Labeling Tasks Vikas C. Raykar, Shipeng Yu
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Rapid Deformable Object Detection Using Dual-Tree Branch-and-Bound Iasonas Kokkinos
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Reconstructing Patterns of Information Diffusion from Incomplete Observations Flavio Chierichetti, David Liben-nowell, Jon M. Kleinberg
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Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance Carsten Rother, Martin Kiefel, Lumin Zhang, Bernhard Schölkopf, Peter V. Gehler
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Regularized Laplacian Estimation and Fast Eigenvector Approximation Patrick O. Perry, Michael W. Mahoney
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Reinforcement Learning Using Kernel-Based Stochastic Factorization Andre Barreto, Doina Precup, Joelle Pineau
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Relative Density-Ratio Estimation for Robust Distribution Comparison Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama
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Robust Lasso with Missing and Grossly Corrupted Observations Nasser M. Nasrabadi, Trac D. Tran, Nam Nguyen
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Robust Multi-Class Gaussian Process Classification Daniel Hernández-lobato, Jose M. Hernández-lobato, Pierre Dupont
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RTRMC: A Riemannian Trust-Region Method for Low-Rank Matrix Completion Nicolas Boumal, Pierre-antoine Absil
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Scalable Training of Mixture Models via Coresets Dan Feldman, Matthew Faulkner, Andreas Krause
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See the Tree Through the Lines: The Shazoo Algorithm Fabio Vitale, Nicolò Cesa-bianchi, Claudio Gentile, Giovanni Zappella
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Select and Sample - A Model of Efficient Neural Inference and Learning Jacquelyn A. Shelton, Abdul S. Sheikh, Pietro Berkes, Joerg Bornschein, Jörg Lücke
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Selecting Receptive Fields in Deep Networks Adam Coates, Andrew Y. Ng
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Selecting the State-Representation in Reinforcement Learning Odalric-ambrym Maillard, Daniil Ryabko, Rémi Munos
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Selective Prediction of Financial Trends with Hidden Markov Models Dmitry Pidan, Ran El-Yaniv
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Semantic Labeling of 3D Point Clouds for Indoor Scenes Hema S. Koppula, Abhishek Anand, Thorsten Joachims, Ashutosh Saxena
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Semi-Supervised Regression via Parallel Field Regularization Binbin Lin, Chiyuan Zhang, Xiaofei He
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Sequence Learning with Hidden Units in Spiking Neural Networks Johanni Brea, Walter Senn, Jean-pascal Pfister
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Shallow vs. Deep Sum-Product Networks Olivier Delalleau, Yoshua Bengio
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Shaping Level Sets with Submodular Functions Francis R. Bach
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ShareBoost: Efficient Multiclass Learning with Feature Sharing Shai Shalev-shwartz, Yonatan Wexler, Amnon Shashua
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Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment Sebastian A. Kurtek, Anuj Srivastava, Wei Wu
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Similarity-Based Learning via Data Driven Embeddings Purushottam Kar, Prateek Jain
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Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC Trung T. Pham, Tat-jun Chin, Jin Yu, David Suter
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Solving Decision Problems with Limited Information Denis D. Maua, Cassio Campos
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SpaRCS: Recovering Low-Rank and Sparse Matrices from Compressive Measurements Andrew E. Waters, Aswin C. Sankaranarayanan, Richard Baraniuk
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Sparse Bayesian Multi-Task Learning Shengbo Guo, Onno Zoeter, Cédric Archambeau
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Sparse Estimation with Structured Dictionaries David P. Wipf
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Sparse Features for PCA-like Linear Regression Christos Boutsidis, Petros Drineas, Malik Magdon-Ismail
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Sparse Filtering Jiquan Ngiam, Zhenghao Chen, Sonia A. Bhaskar, Pang W. Koh, Andrew Y. Ng
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Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation Cho-jui Hsieh, Inderjit S. Dhillon, Pradeep K. Ravikumar, Mátyás A. Sustik
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Sparse Manifold Clustering and Embedding Ehsan Elhamifar, René Vidal
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Sparse Recovery by Thresholded Non-Negative Least Squares Martin Slawski, Matthias Hein
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Sparse Recovery with Brownian Sensing Alexandra Carpentier, Odalric-ambrym Maillard, Rémi Munos
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Spatial Distance Dependent Chinese Restaurant Processes for Image Segmentation Soumya Ghosh, Andrei B. Ungureanu, Erik B. Sudderth, David M. Blei
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Spectral Methods for Learning Multivariate Latent Tree Structure Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang
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Speedy Q-Learning Mohammad Ghavamzadeh, Hilbert J. Kappen, Mohammad G. Azar, Rémi Munos
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Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning Michalis K. Titsias, Miguel Lázaro-Gredilla
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Statistical Performance of Convex Tensor Decomposition Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, Hisashi Kashima
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Statistical Tests for Optimization Efficiency Levi Boyles, Anoop Korattikara, Deva Ramanan, Max Welling
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Stochastic Convex Optimization with Bandit Feedback Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
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Structural Equations and Divisive Normalization for Energy-Dependent Component Analysis Jun-ichiro Hirayama, Aapo Hyvärinen
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Structure Learning for Optimization Shulin Yang, Ali Rahimi
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Structured Learning for Cell Tracking Xinghua Lou, Fred A. Hamprecht
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Structured Sparse Coding via Lateral Inhibition Arthur D. Szlam, Karol Gregor, Yann L. Cun
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Submodular Multi-Label Learning James Petterson, Tibério S. Caetano
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T-Divergence Based Approximate Inference Nan Ding, Yuan Qi, S.v.n. Vishwanathan
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Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification Ichiro Takeuchi, Masashi Sugiyama
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TD_gamma: Re-Evaluating Complex Backups in Temporal Difference Learning George Konidaris, Scott Niekum, Philip S. Thomas
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Testing a Bayesian Measure of Representativeness Using a Large Image Database Joshua T. Abbott, Katherine A. Heller, Zoubin Ghahramani, Thomas L. Griffiths
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The Doubly Correlated Nonparametric Topic Model Dae I. Kim, Erik B. Sudderth
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The Fast Convergence of Boosting Matus J. Telgarsky
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The Fixed Points of Off-Policy TD J. Z. Kolter
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The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers Luca Oneto, Davide Anguita, Alessandro Ghio, Sandro Ridella
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The Kernel Beta Process Lu Ren, Yingjian Wang, Lawrence Carin, David B. Dunson
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The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning Marius Kloft, Gilles Blanchard
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The Manifold Tangent Classifier Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio, Xavier Muller
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Thinning Measurement Models and Questionnaire Design Ricardo Silva
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Trace Lasso: A Trace Norm Regularization for Correlated Designs Edouard Grave, Guillaume R. Obozinski, Francis R. Bach
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Transfer from Multiple MDPs Alessandro Lazaric, Marcello Restelli
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Transfer Learning by Borrowing Examples for Multiclass Object Detection Joseph J. Lim, Ruslan Salakhutdinov, Antonio Torralba
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Two Is Better than One: Distinct Roles for Familiarity and Recollection in Retrieving Palimpsest Memories Cristina Savin, Peter Dayan, Máté Lengyel
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Understanding the Intrinsic Memorability of Images Phillip Isola, Devi Parikh, Antonio Torralba, Aude Oliva
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Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning Taiji Suzuki
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Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction Siwei Lyu
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Uniqueness of Belief Propagation on Signed Graphs Yusuke Watanabe
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Universal Low-Rank Matrix Recovery from Pauli Measurements Yi-kai Liu
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Unsupervised Learning Models of Primary Cortical Receptive Fields and Receptive Field Plasticity Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Saxe, Andrew Y. Ng
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Variance Penalizing AdaBoost Pannagadatta K. Shivaswamy, Tony Jebara
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Variance Reduction in Monte-Carlo Tree Search Joel Veness, Marc Lanctot, Michael Bowling
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Variational Gaussian Process Dynamical Systems Andreas Damianou, Michalis K. Titsias, Neil D. Lawrence
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Variational Learning for Recurrent Spiking Networks Danilo J. Rezende, Daan Wierstra, Wulfram Gerstner
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Video Annotation and Tracking with Active Learning Carl Vondrick, Deva Ramanan
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Why the Brain Separates Face Recognition from Object Recognition Joel Z. Leibo, Jim Mutch, Tomaso Poggio
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