Book

Advances in Neural Information Processing Systems 24 (NIPS 2011)

Edited by: J. Shawe-Taylor and R. Zemel and P. Bartlett and F. Pereira and K.Q. Weinberger

ISBN: 9781618395993

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

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