NIPS 2011 Proceedings

Advances in Neural Information Processing Systems 24 (NIPS 2011)

The papers below appear in Advances in Neural Information Processing Systems 24 edited by J. Shawe-Taylor and R.S. Zemel and P.L. Bartlett and F. Pereira and K.Q. Weinberger.
They are proceedings from the conference, "Neural Information Processing Systems 2011."

Maximum Margin Multi-Instance Learning Hua Wang, Heng Huang, Farhad Kamangar, Feiping Nie, Chris H. Ding
Shaping Level Sets with Submodular Functions Francis R. Bach
Nonlinear Inverse Reinforcement Learning with Gaussian Processes Sergey Levine, Zoran Popovic, Vladlen Koltun
Video Annotation and Tracking with Active Learning Carl Vondrick, Deva Ramanan
On U-processes and clustering performance Stéphan J. Clémençcon
Penalty Decomposition Methods for Rank Minimization Yong Zhang, Zhaosong Lu
Sparse Manifold Clustering and Embedding Ehsan Elhamifar, René Vidal
Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction Siwei Lyu
Image Parsing with Stochastic Scene Grammar Yibiao Zhao, Song-chun Zhu
A Reinforcement Learning Theory for Homeostatic Regulation Mehdi Keramati, Boris S. Gutkin
Learning large-margin halfspaces with more malicious noise Phil Long, Rocco Servedio
On Strategy Stitching in Large Extensive Form Multiplayer Games Richard G. Gibson, Duane Szafron
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials Philipp Krähenbühl, Vladlen Koltun
Transfer Learning by Borrowing Examples for Multiclass Object Detection Joseph J. Lim, Ruslan R. Salakhutdinov, Antonio Torralba
Environmental statistics and the trade-off between model-based and TD learning in humans Dylan A. Simon, Nathaniel D. Daw
Variational Learning for Recurrent Spiking Networks Danilo J. Rezende, Daan Wierstra, Wulfram Gerstner
Multiple Instance Learning on Structured Data Dan Zhang, Yan Liu, Luo Si, Jian Zhang, Richard D. Lawrence
Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds Nitesh Shroff, Pavan Turaga, Rama Chellappa
A Global Structural EM Algorithm for a Model of Cancer Progression Ali Tofigh, Erik Sj̦lund, Mattias H̦glund, Jens Lagergren
Action-Gap Phenomenon in Reinforcement Learning Amir-massoud Farahmand
Generalized Lasso based Approximation of Sparse Coding for Visual Recognition Nobuyuki Morioka, Shin'ichi Satoh
Matrix Completion for Multi-label Image Classification Ricardo S. Cabral, Fernando Torre, Joao P. Costeira, Alexandre Bernardino
Multi-View Learning of Word Embeddings via CCA Paramveer Dhillon, Dean P. Foster, Lyle H. Ungar
Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent Shinichi Nakajima, Masashi Sugiyama, S. D. Babacan
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
Additive Gaussian Processes David K. Duvenaud, Hannes Nickisch, Carl E. Rasmussen
Inferring Interaction Networks using the IBP applied to microRNA Target Prediction Hai-son P. Le, Ziv Bar-joseph
Semantic Labeling of 3D Point Clouds for Indoor Scenes Hema S. Koppula, Abhishek Anand, Thorsten Joachims, Ashutosh Saxena
Learning Higher-Order Graph Structure with Features by Structure Penalty Shilin Ding, Grace Wahba, Xiaojin Zhu
Analysis and Improvement of Policy Gradient Estimation Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama
Dimensionality Reduction Using the Sparse Linear Model Ioannis A. Gkioulekas, Todd Zickler
Robust Multi-Class Gaussian Process Classification Daniel Hernández-lobato, Jose M. Hernández-lobato, Pierre Dupont
Maximum Margin Multi-Label Structured Prediction Christoph H. Lampert
Extracting Speaker-Specific Information with a Regularized Siamese Deep Network Ke Chen, Ahmad Salman
Thinning Measurement Models and Questionnaire Design Ricardo Silva
Inductive reasoning about chimeric creatures Charles Kemp
Optimal Reinforcement Learning for Gaussian Systems Philipp Hennig
A Denoising View of Matrix Completion Weiran Wang, Miguel Á. Carreira-Perpiñán, Zhengdong Lu
Efficient Online Learning via Randomized Rounding Nicolò Cesa-bianchi, Ohad Shamir
Efficient Methods for Overlapping Group Lasso Lei Yuan, Jun Liu, Jieping Ye
Differentially Private M-Estimators Jing Lei
Multiple Instance Filtering Kamil A. Wnuk, Stefano Soatto
Phase transition in the family of p-resistances Morteza Alamgir, Ulrike V. Luxburg
Convergent Bounds on the Euclidean Distance Yoonho Hwang, Hee-kap Ahn
Heavy-tailed Distances for Gradient Based Image Descriptors Yangqing Jia, Trevor Darrell
RTRMC: A Riemannian trust-region method for low-rank matrix completion Nicolas Boumal, Pierre-antoine Absil
Expressive Power and Approximation Errors of Restricted Boltzmann Machines Guido F. Montufar, Johannes Rauh, Nihat Ay
History distribution matching method for predicting effectiveness of HIV combination therapies Jasmina Bogojeska
Semi-supervised Regression via Parallel Field Regularization Binbin Lin, Chiyuan Zhang, Xiaofei He
Object Detection with Grammar Models Ross B. Girshick, Pedro F. Felzenszwalb, David A. McAllester
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning Eric Moulines, Francis R. Bach
On fast approximate submodular minimization Stefanie Jegelka, Hui Lin, Jeff A. Bilmes
Emergence of Multiplication in a Biophysical Model of a Wide-Field Visual Neuron for Computing Object Approaches: Dynamics, Peaks, & Fits Matthias S. Keil
Efficient anomaly detection using bipartite k-NN graphs Kumar Sricharan, Alfred O. Hero
Projection onto A Nonnegative Max-Heap Jun Liu, Liang Sun, Jieping Ye
Improving Topic Coherence with Regularized Topic Models David Newman, Edwin V. Bonilla, Wray Buntine
A Two-Stage Weighting Framework for Multi-Source Domain Adaptation Qian Sun, Rita Chattopadhyay, Sethuraman Panchanathan, Jieping Ye
An ideal observer model for identifying the reference frame of objects Joseph L. Austerweil, Abram L. Friesen, Thomas L. Griffiths
Generalized Beta Mixtures of Gaussians Artin Armagan, Merlise Clyde, David B. Dunson
Large-Scale Sparse Principal Component Analysis with Application to Text Data Youwei Zhang, Laurent E. Ghaoui
Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC Trung T. Pham, Tat-jun Chin, Jin Yu, David Suter
\theta-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene Understanding Congcong Li, Ashutosh Saxena, Tsuhan Chen
Crowdclustering Ryan G. Gomes, Peter Welinder, Andreas Krause, Pietro Perona
Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition Jia Deng, Sanjeev Satheesh, Alexander C. Berg, Fei Li
Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification Ichiro Takeuchi, Masashi Sugiyama
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers Luca Oneto, Davide Anguita, Alessandro Ghio, Sandro Ridella
Relative Density-Ratio Estimation for Robust Distribution Comparison Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama
Solving Decision Problems with Limited Information Denis D. Maua, Cassio Campos
Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation Zhouchen Lin, Risheng Liu, Zhixun Su
Learning a Tree of Metrics with Disjoint Visual Features Kristen Grauman, Fei Sha, Sung Ju Hwang
Efficient inference in matrix-variate Gaussian models with \iid observation noise Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten M. Borgwardt
On Causal Discovery with Cyclic Additive Noise Models Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf
Learning to Agglomerate Superpixel Hierarchies Viren Jain, Srinivas C. Turaga, K Briggman, Moritz N. Helmstaedter, Winfried Denk, H. S. Seung
A Convergence Analysis of Log-Linear Training Simon Wiesler, Hermann Ney
Shallow vs. Deep Sum-Product Networks Olivier Delalleau, Yoshua Bengio
Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment Sebastian A. Kurtek, Anuj Srivastava, Wei Wu
From Bandits to Experts: On the Value of Side-Observations Shie Mannor, Ohad Shamir
Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent Benjamin Recht, Christopher Re, Stephen Wright, Feng Niu
Clustered Multi-Task Learning Via Alternating Structure Optimization Jiayu Zhou, Jianhui Chen, Jieping Ye
Why The Brain Separates Face Recognition From Object Recognition Joel Z. Leibo, Jim Mutch, Tomaso Poggio
Reinforcement Learning using Kernel-Based Stochastic Factorization Andre S. Barreto, Doina Precup, Joelle Pineau
k-NN Regression Adapts to Local Intrinsic Dimension Samory Kpotufe
Learning unbelievable probabilities Xaq Pitkow, Yashar Ahmadian, Ken D. Miller
A Machine Learning Approach to Predict Chemical Reactions Matthew A. Kayala, Pierre F. Baldi
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
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance Carsten Rother, Martin Kiefel, Lumin Zhang, Bernhard Schölkopf, Peter V. Gehler
Probabilistic Modeling of Dependencies Among Visual Short-Term Memory Representations Emin Orhan, Robert A. Jacobs
Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness Rémi Munos
Reconstructing Patterns of Information Diffusion from Incomplete Observations Flavio Chierichetti, David Liben-nowell, Jon M. Kleinberg
Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection Richard Socher, Eric H. Huang, Jeffrey Pennin, Christopher D. Manning, Andrew Y. Ng
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity Nir Ailon
Modelling Genetic Variations using Fragmentation-Coagulation Processes Yee W. Teh, Charles Blundell, Lloyd Elliott
Prediction strategies without loss Michael Kapralov, Rina Panigrahy
Data Skeletonization via Reeb Graphs Xiaoyin Ge, Issam I. Safa, Mikhail Belkin, Yusu Wang
Information Rates and Optimal Decoding in Large Neural Populations Kamiar R. Rad, Liam Paninski
Selective Prediction of Financial Trends with Hidden Markov Models Dmitry Pidan, Ran El-Yaniv
Maximal Cliques that Satisfy Hard Constraints with Application to Deformable Object Model Learning Xinggang Wang, Xiang Bai, Xingwei Yang, Wenyu Liu, Longin J. Latecki
Distributed Delayed Stochastic Optimization Alekh Agarwal, John C. Duchi
Greedy Algorithms for Structurally Constrained High Dimensional Problems Ambuj Tewari, Pradeep K. Ravikumar, Inderjit S. Dhillon
Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction Elad Hazan, Satyen Kale
Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries Zhen J. Xiang, Hao Xu, Peter J. Ramadge
Minimax Localization of Structural Information in Large Noisy Matrices Mladen Kolar, Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh
Maximum Covariance Unfolding : Manifold Learning for Bimodal Data Vijay Mahadevan, Chi W. Wong, Jose C. Pereira, Tom Liu, Nuno Vasconcelos, Lawrence K. Saul
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression Sham M. Kakade, Varun Kanade, Ohad Shamir, Adam Kalai
On the Analysis of Multi-Channel Neural Spike Data Bo Chen, David E. Carlson, Lawrence Carin
Learning Eigenvectors for Free Wouter M. Koolen, Wojciech Kotlowski, Manfred K. Warmuth
Noise Thresholds for Spectral Clustering Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh
The Kernel Beta Process Lu Ren, Yingjian Wang, Lawrence Carin, David B. Dunson
Statistical Performance of Convex Tensor Decomposition Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, Hisashi Kashima
Probabilistic amplitude and frequency demodulation Richard Turner, Maneesh Sahani
Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators Dominique C. Perrault-joncas, Marina Meila
Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons Yan Karklin, Eero P. Simoncelli
Complexity of Inference in Latent Dirichlet Allocation David Sontag, Dan Roy
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning Quoc V. Le, Alexandre Karpenko, Jiquan Ngiam, Andrew Y. Ng
Lower Bounds for Passive and Active Learning Maxim Raginsky, Alexander Rakhlin
Stochastic convex optimization with bandit feedback Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
Structure Learning for Optimization Shulin Yang, Ali Rahimi
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
Active Classification based on Value of Classifier Tianshi Gao, Daphne Koller
Group Anomaly Detection using Flexible Genre Models Liang Xiong, Barnabás Póczos, Jeff G. Schneider
Approximating Semidefinite Programs in Sublinear Time Dan Garber, Elad Hazan
SpaRCS: Recovering low-rank and sparse matrices from compressive measurements Andrew E. Waters, Aswin C. Sankaranarayanan, Richard Baraniuk
Budgeted Optimization with Concurrent Stochastic-Duration Experiments Javad Azimi, Alan Fern, Xiaoli Z. Fern
Online Submodular Set Cover, Ranking, and Repeated Active Learning Andrew Guillory, Jeff A. Bilmes
Structured sparse coding via lateral inhibition Arthur D. Szlam, Karol Gregor, Yann L. Cun
Sparse Filtering Jiquan Ngiam, Zhenghao Chen, Sonia A. Bhaskar, Pang W. Koh, Andrew Y. Ng
Divide-and-Conquer Matrix Factorization Lester W. Mackey, Michael I. Jordan, Ameet Talwalkar
Im2Text: Describing Images Using 1 Million Captioned Photographs Vicente Ordonez, Girish Kulkarni, Tamara L. Berg
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference David Wingate, Noah Goodman, Andreas Stuhlmueller, Jeffrey M. Siskind
Collective Graphical Models Daniel R. Sheldon, Thomas G. Dietterich
Metric Learning with Multiple Kernels Jun Wang, Huyen T. Do, Adam Woznica, Alexandros Kalousis
ShareBoost: Efficient multiclass learning with feature sharing Shai Shalev-shwartz, Yonatan Wexler, Amnon Shashua
Active dendrites: adaptation to spike-based communication Balazs B. Ujfalussy, Máté Lengyel
Message-Passing for Approximate MAP Inference with Latent Variables Jiarong Jiang, Piyush Rai, Hal Daume
A More Powerful Two-Sample Test in High Dimensions using Random Projection Miles Lopes, Laurent Jacob, Martin J. Wainwright
Orthogonal Matching Pursuit with Replacement Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon
Composite Multiclass Losses Elodie Vernet, Mark D. Reid, Robert C. Williamson
Beating SGD: Learning SVMs in Sublinear Time Elad Hazan, Tomer Koren, Nati Srebro
Greedy Model Averaging Dong Dai, Tong Zhang
Large-Scale Category Structure Aware Image Categorization Bin Zhao, Fei Li, Eric P. Xing
On the accuracy of l1-filtering of signals with block-sparse structure Fatma K. Karzan, Arkadi S. Nemirovski, Boris T. Polyak, Anatoli Juditsky
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
Finite Time Analysis of Stratified Sampling for Monte Carlo Alexandra Carpentier, Rémi Munos
Monte Carlo Value Iteration with Macro-Actions Zhan Lim, Lee Sun, Daniel J. Hsu
Structured Learning for Cell Tracking Xinghua Lou, Fred A. Hamprecht
Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories Cristina Savin, Peter Dayan, Máté Lengyel
Algorithms and hardness results for parallel large margin learning Phil Long, Rocco Servedio
Portmanteau Vocabularies for Multi-Cue Image Representation Fahad S. Khan, Joost Weijer, Andrew D. Bagdanov, Maria Vanrell
Boosting with Maximum Adaptive Sampling Charles Dubout, Francois Fleuret
Gaussian Process Training with Input Noise Andrew Mchutchon, Carl E. Rasmussen
Empirical models of spiking in neural populations Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities Angela Yao, Juergen Gall, Luc V. Gool, Raquel Urtasun
Bayesian Partitioning of Large-Scale Distance Data David Adametz, Volker Roth
From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models Skander Mensi, Richard Naud, Wulfram Gerstner
On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference Guy Broeck
Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices Xianxing Zhang, Lawrence Carin, David B. Dunson
An Exact Algorithm for F-Measure Maximization Krzysztof J. Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier
Co-regularized Multi-view Spectral Clustering Abhishek Kumar, Piyush Rai, Hal Daume
Sequence learning with hidden units in spiking neural networks Johanni Brea, Walter Senn, Jean-pascal Pfister
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
A blind sparse deconvolution method for neural spike identification Chaitanya Ekanadham, Daniel Tranchina, Eero P. Simoncelli
How Do Humans Teach: On Curriculum Learning and Teaching Dimension Faisal Khan, Bilge Mutlu, Xiaojin Zhu
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization Mark Schmidt, Nicolas L. Roux, Francis R. Bach
Joint 3D Estimation of Objects and Scene Layout Andreas Geiger, Christian Wojek, Raquel Urtasun
Spatial distance dependent Chinese restaurant processes for image segmentation Soumya Ghosh, Andrei B. Ungureanu, Erik B. Sudderth, David M. Blei
Pylon Model for Semantic Segmentation Victor Lempitsky, Andrea Vedaldi, Andrew Zisserman
t-divergence Based Approximate Inference Nan Ding, Yuan Qi, S.v.n. Vishwanathan
Learning person-object interactions for action recognition in still images Vincent Delaitre, Josef Sivic, Ivan Laptev
Submodular Multi-Label Learning James Petterson, Tibério S. Caetano
Uniqueness of Belief Propagation on Signed Graphs Yusuke Watanabe
Higher-Order Correlation Clustering for Image Segmentation Sungwoong Kim, Sebastian Nowozin, Pushmeet Kohli, Chang D. Yoo
Optimal learning rates for least squares SVMs using Gaussian kernels Mona Eberts, Ingo Steinwart
Learning Auto-regressive Models from Sequence and Non-sequence Data Tzu-kuo Huang, Jeff G. Schneider
Committing Bandits Loc X. Bui, Ramesh Johari, Shie Mannor
Energetically Optimal Action Potentials Martin B. Stemmler, Biswa Sengupta, Simon Laughlin, Jeremy Niven
Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning Taiji Suzuki
See the Tree Through the Lines: The Shazoo Algorithm Fabio Vitale, Nicolò Cesa-bianchi, Claudio Gentile, Giovanni Zappella
The Fast Convergence of Boosting Matus J. Telgarsky
Multi-armed bandits on implicit metric spaces Aleksandrs Slivkins
Learning Anchor Planes for Classification Ziming Zhang, Lubor Ladicky, Philip Torr, Amir Saffari
Infinite Latent SVM for Classification and Multi-task Learning Jun Zhu, Ning Chen, Eric P. Xing
Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines Matthew D. Zeiler, Graham W. Taylor, Leonid Sigal, Iain Matthews, Rob Fergus
Universal low-rank matrix recovery from Pauli measurements Yi-kai Liu
Better Mini-Batch Algorithms via Accelerated Gradient Methods Andrew Cotter, Ohad Shamir, Nati Srebro, Karthik Sridharan
Adaptive Hedge Tim V. Erven, Wouter M. Koolen, Steven D. Rooij, Peter Grünwald
Agnostic Selective Classification Yair Wiener, Ran El-Yaniv
Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs Armen Allahverdyan, Aram Galstyan
PAC-Bayesian Analysis of Contextual Bandits Yevgeny Seldin, Peter Auer, John S. Shawe-taylor, Ronald Ortner, François Laviolette
Bayesian Spike-Triggered Covariance Analysis Il Memming Park, Jonathan W. Pillow
Non-conjugate Variational Message Passing for Multinomial and Binary Regression David A. Knowles, Tom Minka
Learning to Search Efficiently in High Dimensions Zhen Li, Huazhong Ning, Liangliang Cao, Tong Zhang, Yihong Gong, Thomas S. Huang
A Non-Parametric Approach to Dynamic Programming Oliver B. Kroemer, Jan R. Peters
Advice Refinement in Knowledge-Based SVMs Gautam Kunapuli, Richard Maclin, Jude W. Shavlik
Kernel Bayes' Rule Kenji Fukumizu, Le Song, Arthur Gretton
Transfer from Multiple MDPs Alessandro Lazaric, Marcello Restelli
Sparse Bayesian Multi-Task Learning Shengbo Guo, Onno Zoeter, Cédric Archambeau
Online Learning: Stochastic, Constrained, and Smoothed Adversaries Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint Kenji Fukumizu, Gert R. Lanckriet, Bharath K. Sriperumbudur
Sparse Recovery with Brownian Sensing Alexandra Carpentier, Odalric-ambrym Maillard, Rémi Munos
An Unsupervised Decontamination Procedure For Improving The Reliability Of Human Judgments Michael C. Mozer, Benjamin Link, Harold Pashler
Bayesian Bias Mitigation for Crowdsourcing Fabian L. Wauthier, Michael I. Jordan
Ranking annotators for crowdsourced labeling tasks Vikas C. Raykar, Shipeng Yu
Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery Scott Niekum, Andrew G. Barto
Probabilistic Joint Image Segmentation and Labeling Adrian Ion, Joao Carreira, Cristian Sminchisescu
Variance Reduction in Monte-Carlo Tree Search Joel Veness, Marc Lanctot, Michael Bowling
Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors Hsiu-chin Lin, Vickie Baracos, Russell Greiner, Chun-nam J. Yu
An Application of Tree-Structured Expectation Propagation for Channel Decoding Pablo M. Olmos, Luis Salamanca, Juan Fuentes, Fernando Pérez-Cruz
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions Animashree Anandkumar, Vincent Tan, Alan S. Willsky
Structural equations and divisive normalization for energy-dependent component analysis Jun-ichiro Hirayama, Aapo Hyvärinen
Robust Lasso with missing and grossly corrupted observations Nasser M. Nasrabadi, Trac D. Tran, Nam Nguyen
A concave regularization technique for sparse mixture models Martin O. Larsson, Johan Ugander
Learning a Distance Metric from a Network Blake Shaw, Bert Huang, Tony Jebara
Variance Penalizing AdaBoost Pannagadatta K. Shivaswamy, Tony Jebara
Efficient Offline Communication Policies for Factored Multiagent POMDPs João V. Messias, Matthijs Spaan, Pedro U. Lima
Sparse recovery by thresholded non-negative least squares Martin Slawski, Matthias Hein
On Learning Discrete Graphical Models using Greedy Methods Ali Jalali, Christopher C. Johnson, Pradeep K. Ravikumar
Policy Gradient Coagent Networks Philip S. Thomas
Iterative Learning for Reliable Crowdsourcing Systems David R. Karger, Sewoong Oh, Devavrat Shah
A Model for Temporal Dependencies in Event Streams Asela Gunawardana, Christopher Meek, Puyang Xu
Unsupervised learning models of primary cortical receptive fields and receptive field plasticity Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Saxe, Andrew Y. Ng
The Doubly Correlated Nonparametric Topic Model Dae I. Kim, Erik B. Sudderth
MAP Inference for Bayesian Inverse Reinforcement Learning Jaedeug Choi, Kee-eung Kim
Similarity-based Learning via Data Driven Embeddings Purushottam Kar, Prateek Jain
Predicting Dynamic Difficulty Olana Missura, Thomas Gärtner
Sparse Estimation with Structured Dictionaries David P. Wipf
Spectral Methods for Learning Multivariate Latent Tree Structure Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang
How biased are maximum entropy models? Jakob H. Macke, Iain Murray, Peter E. Latham
Active learning of neural response functions with Gaussian processes Mijung Park, Greg Horwitz, Jonathan W. Pillow
Priors over Recurrent Continuous Time Processes Ardavan Saeedi, Alexandre Bouchard-côté
Learning to Learn with Compound HD Models Antonio Torralba, Joshua B. Tenenbaum, Ruslan R. Salakhutdinov
Anatomically Constrained Decoding of Finger Flexion from Electrocorticographic Signals Zuoguan Wang, Gerwin Schalk, Qiang Ji
Active Learning with a Drifting Distribution Liu Yang
PiCoDes: Learning a Compact Code for Novel-Category Recognition Alessandro Bergamo, Lorenzo Torresani, Andrew W. Fitzgibbon
Confidence Sets for Network Structure David S. Choi, Patrick J. Wolfe, Edo M. Airoldi
Prismatic Algorithm for Discrete D.C. Programming Problem Yoshinobu Kawahara, Takashi Washio
Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms Liefeng Bo, Xiaofeng Ren, Dieter Fox
Multiclass Boosting: Theory and Algorithms Mohammad J. Saberian, Nuno Vasconcelos
Learning with the weighted trace-norm under arbitrary sampling distributions Rina Foygel, Ohad Shamir, Nati Srebro, Ruslan R. Salakhutdinov
Scalable Training of Mixture Models via Coresets Dan Feldman, Matthew Faulkner, Andreas Krause
Generalised Coupled Tensor Factorisation Kenan Y. Yılmaz, Ali T. Cemgil, Umut Simsekli
Nearest Neighbor based Greedy Coordinate Descent Inderjit S. Dhillon, Pradeep K. Ravikumar, Ambuj Tewari
The Fixed Points of Off-Policy TD J. Z. Kolter
Generalizing from Several Related Classification Tasks to a New Unlabeled Sample Gilles Blanchard, Gyemin Lee, Clayton Scott
Trace Lasso: a trace norm regularization for correlated designs Edouard Grave, Guillaume R. Obozinski, Francis R. Bach
Statistical Tests for Optimization Efficiency Levi Boyles, Anoop Korattikara, Deva Ramanan, Max Welling
Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss Joseph Keshet, David A. McAllester
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
Multi-Bandit Best Arm Identification Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric, Sébastien Bubeck
Randomized Algorithms for Comparison-based Search Dominique Tschopp, Suhas Diggavi, Payam Delgosha, Soheil Mohajer
Active Ranking using Pairwise Comparisons Kevin G. Jamieson, Robert Nowak
An Empirical Evaluation of Thompson Sampling Olivier Chapelle, Lihong Li
Blending Autonomous Exploration and Apprenticeship Learning Thomas J. Walsh, Daniel K. Hewlett, Clayton T. Morrison
Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation Onur Dikmen, Cédric Févotte
Evaluating the inverse decision-making approach to preference learning Alan Jern, Christopher G. Lucas, Charles Kemp
Sparse Features for PCA-Like Linear Regression Christos Boutsidis, Petros Drineas, Malik Magdon-Ismail
The Manifold Tangent Classifier Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio, Xavier Muller
Analytical Results for the Error in Filtering of Gaussian Processes Alex K. Susemihl, Ron Meir, Manfred Opper
Improved Algorithms for Linear Stochastic Bandits Yasin Abbasi-yadkori, Dávid Pál, Csaba Szepesvári
Testing a Bayesian Measure of Representativeness Using a Large Image Database Joshua T. Abbott, Katherine A. Heller, Zoubin Ghahramani, Thomas L. Griffiths
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation Cho-jui Hsieh, Inderjit S. Dhillon, Pradeep K. Ravikumar, Mátyás A. Sustik
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning Michalis K. Titsias, Miguel Lázaro-Gredilla
Practical Variational Inference for Neural Networks Alex Graves
Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability David P. Reichert, Peggy Series, Amos J. Storkey
Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts Matthias Hein, Simon Setzer
Fast and Accurate k-means For Large Datasets Michael Shindler, Alex Wong, Adam W. Meyerson
A rational model of causal inference with continuous causes Thomas L. Griffiths, Michael James
Quasi-Newton Methods for Markov Chain Monte Carlo Yichuan Zhang, Charles A. Sutton
TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning George Konidaris, Scott Niekum, Philip S. Thomas
Speedy Q-Learning Mohammad Ghavamzadeh, Hilbert J. Kappen, Mohammad G. Azar, Rémi Munos
Regularized Laplacian Estimation and Fast Eigenvector Approximation Patrick O. Perry, Michael W. Mahoney
Understanding the Intrinsic Memorability of Images Phillip Isola, Devi Parikh, Antonio Torralba, Aude Oliva
The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning Marius Kloft, Gilles Blanchard
Contextual Gaussian Process Bandit Optimization Andreas Krause, Cheng S. Ong
Co-Training for Domain Adaptation Minmin Chen, Kilian Q. Weinberger, John Blitzer
Autonomous Learning of Action Models for Planning Neville Mehta, Prasad Tadepalli, Alan Fern
Gaussian process modulated renewal processes Yee W. Teh, Vinayak Rao
Linear Submodular Bandits and their Application to Diversified Retrieval Yisong Yue, Carlos Guestrin
Continuous-Time Regression Models for Longitudinal Networks Duy Q. Vu, David Hunter, Padhraic Smyth, Arthur U. Asuncion
On Tracking The Partition Function Guillaume Desjardins, Yoshua Bengio, Aaron C. Courville
Variational Gaussian Process Dynamical Systems Andreas Damianou, Michalis K. Titsias, Neil D. Lawrence
Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels Vikas Sindhwani, Aurelie C. Lozano
Selecting Receptive Fields in Deep Networks Adam Coates, Andrew Y. Ng
Convergent Fitted Value Iteration with Linear Function Approximation Daniel J. Lizotte
Algorithms for Hyper-Parameter Optimization James S. Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl
Neural Reconstruction with Approximate Message Passing (NeuRAMP) Alyson K. Fletcher, Sundeep Rangan, Lav R. Varshney, Aniruddha Bhargava
Query-Aware MCMC Michael L. Wick, Andrew McCallum
A reinterpretation of the policy oscillation phenomenon in approximate policy iteration Paul Wagner
Inferring spike-timing-dependent plasticity from spike train data Ian Stevenson, Konrad Koerding
Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints Omar Z. Khan, Pascal Poupart, John-mark M. Agosta
A Collaborative Mechanism for Crowdsourcing Prediction Problems Jacob D. Abernethy, Rafael M. Frongillo
Hierarchically Supervised Latent Dirichlet Allocation Adler J. Perotte, Frank Wood, Noemie Elhadad, Nicholas Bartlett
Select and Sample - A Model of Efficient Neural Inference and Learning Jacquelyn A. Shelton, Abdul S. Sheikh, Pietro Berkes, Joerg Bornschein, Joerg Luecke
Selecting the State-Representation in Reinforcement Learning Odalric-ambrym Maillard, Daniil Ryabko, Rémi Munos
Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning Joni K. Pajarinen, Jaakko Peltonen
On the Universality of Online Mirror Descent Nati Srebro, Karthik Sridharan, Ambuj Tewari
Demixed Principal Component Analysis Wieland Brendel, Ranulfo Romo, Christian K. Machens
EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning Feng Yan, Yuan Qi
Hashing Algorithms for Large-Scale Learning Ping Li, Anshumali Shrivastava, Joshua L. Moore, Arnd C. König
Rapid Deformable Object Detection using Dual-Tree Branch-and-Bound Iasonas Kokkinos
Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation Nico Goernitz, Christian Widmer, Georg Zeller, Andre Kahles, Gunnar Rätsch, Sören Sonnenburg
Predicting response time and error rates in visual search Bo Chen, Vidhya Navalpakkam, Pietro Perona
Kernel Embeddings of Latent Tree Graphical Models Le Song, Eric P. Xing, Ankur P. Parikh
Inference in continuous-time change-point models Florian Stimberg, Manfred Opper, Guido Sanguinetti, Andreas Ruttor
High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity Po-ling Loh, Martin J. Wainwright
Exploiting spatial overlap to efficiently compute appearance distances between image windows Bogdan Alexe, Viviana Petrescu, Vittorio Ferrari
Accelerated Adaptive Markov Chain for Partition Function Computation Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman