Computer_Elearning

【2011-2016】 NIPS汇总 - Advances in Neural Information Processing Systems

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, Prof. 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, Prof. 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, David 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 Chun-Nam Yu, Russell Greiner, Hsiu-Chin Lin, Vickie Baracos
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

Advances in Neural Information Processing Systems 25 (NIPS 2012)

The papers below appear in Advances in Neural Information Processing Systems 25 edited by F. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger .
They are proceedings from the conference, "Neural Information Processing Systems 2012."

Locally Uniform Comparison Image Descriptor Andrew Ziegler, Eric Christiansen, David Kriegman, Serge J. Belongie
Learning from Distributions via Support Measure Machines Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, Prof. Bernhard Schölkopf
Finding Exemplars from Pairwise Dissimilarities via Simultaneous Sparse Recovery Ehsan Elhamifar, Guillermo Sapiro, René Vidal
Feature Clustering for Accelerating Parallel Coordinate Descent Chad Scherrer, Ambuj Tewari, Mahantesh Halappanavar, David Haglin
Multi-scale Hyper-time Hardware Emulation of Human Motor Nervous System Based on Spiking Neurons using FPGA C. M. Niu, Sirish Nandyala, Won J. Sohn, Terence Sanger
Active Learning of Model Evidence Using Bayesian Quadrature Michael Osborne, Roman Garnett, Zoubin Ghahramani, David K. Duvenaud, Stephen J. Roberts, Carl E. Rasmussen
Coupling Nonparametric Mixtures via Latent Dirichlet Processes Dahua Lin, John W. Fisher
Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction Minjie Xu, Jun Zhu, Bo Zhang
Bayesian Hierarchical Reinforcement Learning Feng Cao, Soumya Ray
Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction Christoph H. Lampert
Local Supervised Learning through Space Partitioning Joseph Wang, Venkatesh Saligrama
A Generative Model for Parts-based Object Segmentation S. Eslami, Christopher Williams
Super-Bit Locality-Sensitive Hashing Jianqiu Ji, Jianmin Li, Shuicheng Yan, Bo Zhang, Qi Tian
The Bethe Partition Function of Log-supermodular Graphical Models Nicholas Ruozzi
Random Utility Theory for Social Choice Hossein Azari, David Parks, Lirong Xia
Putting Bayes to sleep Dmitry Adamskiy, Manfred K. Warmuth, Wouter M. Koolen
A new metric on the manifold of kernel matrices with application to matrix geometric means Suvrit Sra
Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification Wei Bi, James T. Kwok
Smooth-projected Neighborhood Pursuit for High-dimensional Nonparanormal Graph Estimation Tuo Zhao, Kathryn Roeder, Han Liu
Semiparametric Principal Component Analysis Fang Han, Han Liu
Coding efficiency and detectability of rate fluctuations with non-Poisson neuronal firing Shinsuke Koyama
The representer theorem for Hilbert spaces: a necessary and sufficient condition Francesco Dinuzzo, Prof. Bernhard Schölkopf
On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking Clément Calauzènes, Nicolas Usunier, Patrick Gallinari
Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress Manuel Lopes, Tobias Lang, Marc Toussaint, Pierre-yves Oudeyer
Supervised Learning with Similarity Functions Purushottam Kar, Prateek Jain
Cocktail Party Processing via Structured Prediction Yuxuan Wang, Deliang Wang
Robustness and risk-sensitivity in Markov decision processes Takayuki Osogami
Dynamical And-Or Graph Learning for Object Shape Modeling and Detection Xiaolong Wang, Liang Lin
Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions Alexandra Carpentier, Rémi Munos
Distributed Non-Stochastic Experts Varun Kanade, Zhenming Liu, Bozidar Radunovic
Learning Image Descriptors with the Boosting-Trick Tomasz Trzcinski, Mario Christoudias, Vincent Lepetit, Pascal Fua
Fast Resampling Weighted v-Statistics Chunxiao Zhou, Jiseong Park, Yun Fu
Multi-task Vector Field Learning Binbin Lin, Sen Yang, Chiyuan Zhang, Jieping Ye, Xiaofei He
Memorability of Image Regions Aditya Khosla, Jianxiong Xiao, Antonio Torralba, Aude Oliva
Nonparametric Bayesian Inverse Reinforcement Learning for Multiple Reward Functions Jaedeug Choi, Kee-eung Kim
Automatic Feature Induction for Stagewise Collaborative Filtering Joonseok Lee, Mingxuan Sun, Seungyeon Kim, Guy Lebanon
Selective Labeling via Error Bound Minimization Quanquan Gu, Tong Zhang, Jiawei Han, Chris H. Ding
Volume Regularization for Binary Classification Koby Crammer, Tal Wagner
Image Denoising and Inpainting with Deep Neural Networks Junyuan Xie, Linli Xu, Enhong Chen
Max-Margin Structured Output Regression for Spatio-Temporal Action Localization Du Tran, Junsong Yuan
Transelliptical Component Analysis Fang Han, Han Liu
Action-Model Based Multi-agent Plan Recognition Hankz H. Zhuo, Qiang Yang, Subbarao Kambhampati
Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity Angela Eigenstetter, Bjorn Ommer
Non-parametric Approximate Dynamic Programming via the Kernel Method Nikhil Bhat, Vivek Farias, Ciamac C. Moallemi
Optimal Regularized Dual Averaging Methods for Stochastic Optimization Xi Chen, Qihang Lin, Javier Pena
The variational hierarchical EM algorithm for clustering hidden Markov models Emanuele Coviello, Gert R. Lanckriet, Antoni B. Chan
Truncation-free Online Variational Inference for Bayesian Nonparametric Models Chong Wang, David M. Blei
3D Social Saliency from Head-mounted Cameras Hyun S. Park, Eakta Jain, Yaser Sheikh
Context-Sensitive Decision Forests for Object Detection Peter Kontschieder, Samuel R. Bulò, Antonio Criminisi, Pushmeet Kohli, Marcello Pelillo, Horst Bischof
Learning Invariant Representations of Molecules for Atomization Energy Prediction Grégoire Montavon, Katja Hansen, Siamac Fazli, Matthias Rupp, Franziska Biegler, Andreas Ziehe, Alexandre Tkatchenko, Anatole V. Lilienfeld, Klaus-Robert Müller
Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button Joan Fruitet, Alexandra Carpentier, Maureen Clerc, Rémi Munos
Multiplicative Forests for Continuous-Time Processes Jeremy Weiss, Sriraam Natarajan, David Page
Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task Jenna Wiens, Eric Horvitz, John V. Guttag
Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison Tianbao Yang, Yu-feng Li, Mehrdad Mahdavi, Rong Jin, Zhi-Hua Zhou
Multiclass Learning Approaches: A Theoretical Comparison with Implications Amit Daniely, Sivan Sabato, Shai S. Shwartz
Stochastic Gradient Descent with Only One Projection Mehrdad Mahdavi, Tianbao Yang, Rong Jin, Shenghuo Zhu, Jinfeng Yi
Neuronal Spike Generation Mechanism as an Oversampling, Noise-shaping A-to-D converter Dmitri B. Chklovskii, Daniel Soudry
Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction Pietro D. Lena, Ken Nagata, Pierre F. Baldi
Assessing Blinding in Clinical Trials Ognjen Arandjelovic
Scalable nonconvex inexact proximal splitting Suvrit Sra
Learning to Discover Social Circles in Ego Networks Jure Leskovec, Julian J. Mcauley
A Conditional Multinomial Mixture Model for Superset Label Learning Liping Liu, Thomas G. Dietterich
Majorization for CRFs and Latent Likelihoods Tony Jebara, Anna Choromanska
Ensemble weighted kernel estimators for multivariate entropy estimation Kumar Sricharan, Alfred O. Hero
Efficient high dimensional maximum entropy modeling via symmetric partition functions Paul Vernaza, Drew Bagnell
Discriminatively Trained Sparse Code Gradients for Contour Detection Ren Xiaofeng, Liefeng Bo
Analyzing 3D Objects in Cluttered Images Mohsen Hejrati, Deva Ramanan
Nonconvex Penalization Using Laplace Exponents and Concave Conjugates Zhihua Zhang, Bojun Tu
3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model Sanja Fidler, Sven Dickinson, Raquel Urtasun
Structured Learning of Gaussian Graphical Models Karthik Mohan, Mike Chung, Seungyeop Han, Daniela Witten, Su-in Lee, Maryam Fazel
A Polylog Pivot Steps Simplex Algorithm for Classification Elad Hazan, Zohar Karnin
Shifting Weights: Adapting Object Detectors from Image to Video Kevin Tang, Vignesh Ramanathan, Li Fei-fei, Daphne Koller
A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound Shusen Wang, Zhihua Zhang
Convolutional-Recursive Deep Learning for 3D Object Classification Richard Socher, Brody Huval, Bharath Bath, Christopher D. Manning, Andrew Y. Ng
Semi-Supervised Domain Adaptation with Non-Parametric Copulas David Lopez-paz, Jose M. Hernández-lobato, Prof. Bernhard Schölkopf
Identification of Recurrent Patterns in the Activation of Brain Networks Firdaus Janoos, Weichang Li, Niranjan Subrahmanya, Istvan Morocz, William Wells
Density-Difference Estimation Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus D. Plessis, Song Liu, Ichiro Takeuchi
Variational Inference for Crowdsourcing Qiang Liu, Jian Peng, Alexander T. Ihler
MCMC for continuous-time discrete-state systems Vinayak Rao, Yee W. Teh
A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling Pieter-jan Kindermans, Hannes Verschore, David Verstraeten, Benjamin Schrauwen
Learning about Canonical Views from Internet Image Collections Elad Mezuman, Yair Weiss
Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data Assaf Glazer, Michael Lindenbaum, Shaul Markovitch
Multiresolution Gaussian Processes Emily Fox, David B. Dunson
Localizing 3D cuboids in single-view images Jianxiong Xiao, Bryan Russell, Antonio Torralba
Newton-Like Methods for Sparse Inverse Covariance Estimation Figen Oztoprak, Jorge Nocedal, Steven Rennie, Peder A. Olsen
Learning to Align from Scratch Gary Huang, Marwan Mattar, Honglak Lee, Erik G. Learned-miller
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints Stefan Habenschuss, Johannes Bill, Bernhard Nessler
Clustering Aggregation as Maximum-Weight Independent Set Nan Li, Longin J. Latecki
Topology Constraints in Graphical Models Marcelo Fiori, Pablo Musé, Guillermo Sapiro
Transelliptical Graphical Models Han Liu, Fang Han, Cun-hui Zhang
Kernel Latent SVM for Visual Recognition Weilong Yang, Yang Wang, Arash Vahdat, Greg Mori
Learning Partially Observable Models Using Temporally Abstract Decision Trees Erik Talvitie
Proximal Newton-type methods for convex optimization Jason D. Lee, Yuekai Sun, Michael Saunders
Regularized Off-Policy TD-Learning Bo Liu, Sridhar Mahadevan, Ji Liu
Multi-criteria Anomaly Detection using Pareto Depth Analysis Ko-jen Hsiao, Kevin Xu, Jeff Calder, Alfred O. Hero
Synchronization can Control Regularization in Neural Systems via Correlated Noise Processes Jake Bouvrie, Jean-jeacques Slotine
Calibrated Elastic Regularization in Matrix Completion Tingni Sun, Cun-hui Zhang
Predicting Action Content On-Line and in Real Time before Action Onset – an Intracranial Human Study Uri Maoz, Shengxuan Ye, Ian Ross, Adam Mamelak, Christof Koch
Searching for objects driven by context Bogdan Alexe, Nicolas Heess, Yee W. Teh, Vittorio Ferrari
Timely Object Recognition Sergey Karayev, Tobias Baumgartner, Mario Fritz, Trevor Darrell
Nonparanormal Belief Propagation (NPNBP) Gal Elidan, Cobi Cario
Deep Representations and Codes for Image Auto-Annotation Ryan Kiros, Csaba Szepesvári
A Spectral Algorithm for Latent Dirichlet Allocation Anima Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-kai Liu
Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs Aharon Birnbaum, Shai S. Shwartz
Matrix reconstruction with the local max norm Rina Foygel, Nathan Srebro, Ruslan R. Salakhutdinov
Analog readout for optical reservoir computers Anteo Smerieri, François Duport, Yvon Paquot, Benjamin Schrauwen, Marc Haelterman, Serge Massar
Accuracy at the Top Stephen Boyd, Corinna Cortes, Mehryar Mohri, Ana Radovanovic
Minimizing Sparse High-Order Energies by Submodular Vertex-Cover Andrew Delong, Olga Veksler, Anton Osokin, Yuri Boykov
Perfect Dimensionality Recovery by Variational Bayesian PCA Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. D. Babacan
Mirror Descent Meets Fixed Share (and feels no regret) Nicolò Cesa-bianchi, Pierre Gaillard, Gabor Lugosi, Gilles Stoltz
Near-optimal Differentially Private Principal Components Kamalika Chaudhuri, Anand Sarwate, Kaushik Sinha
Random function priors for exchangeable arrays with applications to graphs and relational data James Lloyd, Peter Orbanz, Zoubin Ghahramani, Daniel M. Roy
Inverse Reinforcement Learning through Structured Classification Edouard Klein, Matthieu Geist, Bilal Piot, Olivier Pietquin
Augmented-SVM: Automatic space partitioning for combining multiple non-linear dynamics Ashwini Shukla, Aude Billard
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search Arthur Guez, David Silver, Peter Dayan
Dimensionality Dependent PAC-Bayes Margin Bound Chi Jin, Liwei Wang
Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs Anima Anandkumar, Ragupathyraj Valluvan
Learning Mixtures of Tree Graphical Models Anima Anandkumar, Daniel J. Hsu, Furong Huang, Sham M. Kakade
Hamming Distance Metric Learning Mohammad Norouzi, David J. Fleet, Ruslan R. Salakhutdinov
Spiking and saturating dendrites differentially expand single neuron computation capacity Romain Cazé, Mark Humphries, Boris S. Gutkin
Clustering by Nonnegative Matrix Factorization Using Graph Random Walk Zhirong Yang, Tele Hao, Onur Dikmen, Xi Chen, Erkki Oja
Delay Compensation with Dynamical Synapses Chi Fung, K. Wong, Si Wu
ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
Recognizing Activities by Attribute Dynamics Weixin Li, Nuno Vasconcelos
Compressive Sensing MRI with Wavelet Tree Sparsity Chen Chen, Junzhou Huang
Training sparse natural image models with a fast Gibbs sampler of an extended state space Lucas Theis, Jascha Sohl-dickstein, Matthias Bethge
A Bayesian Approach for Policy Learning from Trajectory Preference Queries Aaron Wilson, Alan Fern, Prasad Tadepalli
GenDeR: A Generic Diversified Ranking Algorithm Jingrui He, Hanghang Tong, Qiaozhu Mei, Boleslaw Szymanski
On Multilabel Classification and Ranking with Partial Feedback Claudio Gentile, Francesco Orabona
The Lovász ϑ function, SVMs and finding large dense subgraphs Vinay Jethava, Anders Martinsson, Chiranjib Bhattacharyya, Devdatt Dubhashi
Multi-Task Averaging Sergey Feldman, Maya Gupta, Bela Frigyik
Unsupervised Structure Discovery for Semantic Analysis of Audio Sourish Chaudhuri, Bhiksha Raj
A Marginalized Particle Gaussian Process Regression Yali Wang, Brahim Chaib-draa
Angular Quantization-based Binary Codes for Fast Similarity Search Yunchao Gong, Sanjiv Kumar, Vishal Verma, Svetlana Lazebnik
Optimal kernel choice for large-scale two-sample tests Arthur Gretton, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu, Bharath K. Sriperumbudur
Factoring nonnegative matrices with linear programs Ben Recht, Christopher Re, Joel Tropp, Victor Bittorf
Large Scale Distributed Deep Networks Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc'aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, Quoc V. Le, Andrew Y. Ng
Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space Yanyan Lan, Jiafeng Guo, Xueqi Cheng, Tie-yan Liu
Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination Won H. Kim, Deepti Pachauri, Charles Hatt, Moo. K. Chung, Sterling Johnson, Vikas Singh
A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation Aaron Defazio, Tibério S. Caetano
Fused sparsity and robust estimation for linear models with unknown variance Arnak Dalalyan, Yin Chen
How Prior Probability Influences Decision Making: A Unifying Probabilistic Model Yanping Huang, Timothy Hanks, Mike Shadlen, Abram L. Friesen, Rajesh P. Rao
High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer's Disease Progression Prediction Hua Wang, Feiping Nie, Heng Huang, Jingwen Yan, Sungeun Kim, Shannon Risacher, Andrew Saykin, Li Shen
Symmetric Correspondence Topic Models for Multilingual Text Analysis Kosuke Fukumasu, Koji Eguchi, Eric P. Xing
Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data Michael C. Hughes, Emily Fox, Erik B. Sudderth
Efficient coding provides a direct link between prior and likelihood in perceptual Bayesian inference Xue-xin Wei, Alan A. Stocker
Efficient Sampling for Bipartite Matching Problems Maksims Volkovs, Richard S. Zemel
Learning visual motion in recurrent neural networks Marius Pachitariu, Maneesh Sahani
Learned Prioritization for Trading Off Accuracy and Speed Jiarong Jiang, Adam Teichert, Jason Eisner, Hal Daume
Value Pursuit Iteration Amir M. Farahmand, Doina Precup
Compressive neural representation of sparse, high-dimensional probabilities Xaq Pitkow
Graphical Models via Generalized Linear Models Eunho Yang, Genevera Allen, Zhandong Liu, Pradeep K. Ravikumar
CPRL -- An Extension of Compressive Sensing to the Phase Retrieval Problem Henrik Ohlsson, Allen Yang, Roy Dong, Shankar Sastry
Co-Regularized Hashing for Multimodal Data Yi Zhen, Dit-Yan Yeung
Convergence and Energy Landscape for Cheeger Cut Clustering Xavier Bresson, Thomas Laurent, David Uminsky, James V. Brecht
Symbolic Dynamic Programming for Continuous State and Observation POMDPs Zahra Zamani, Scott Sanner, Pascal Poupart, Kristian Kersting
Bayesian Probabilistic Co-Subspace Addition Lei Shi
Scaled Gradients on Grassmann Manifolds for Matrix Completion Thanh Ngo, Yousef Saad
Q-MKL: Matrix-induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging Chris Hinrichs, Vikas Singh, Jiming Peng, Sterling Johnson
Privacy Aware Learning Martin J. Wainwright, Michael I. Jordan, John C. Duchi
Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods Andre Wibisono, Martin J. Wainwright, Michael I. Jordan, John C. Duchi
Hierarchical Optimistic Region Selection driven by Curiosity Odalric-ambrym Maillard
Sparse Prediction with the k-Support Norm Andreas Argyriou, Rina Foygel, Nathan Srebro
Active Learning of Multi-Index Function Models Tyagi Hemant, Volkan Cevher
Learning Multiple Tasks using Shared Hypotheses Koby Crammer, Yishay Mansour
On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization Doina Precup, Joelle Pineau, Andre S. Barreto
Forward-Backward Activation Algorithm for Hierarchical Hidden Markov Models Kei Wakabayashi, Takao Miura
Communication-Efficient Algorithms for Statistical Optimization Yuchen Zhang, Martin J. Wainwright, John C. Duchi
Identifiability and Unmixing of Latent Parse Trees Daniel J. Hsu, Sham M. Kakade, Percy S. Liang
Bayesian nonparametric models for ranked data Francois Caron, Yee W. Teh
Feature-aware Label Space Dimension Reduction for Multi-label Classification Yao-nan Chen, Hsuan-tien Lin
Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions Alekh Agarwal, Sahand Negahban, Martin J. Wainwright
Graphical Gaussian Vector for Image Categorization Tatsuya Harada, Yasuo Kuniyoshi
Joint Modeling of a Matrix with Associated Text via Latent Binary Features Xianxing Zhang, Lawrence Carin
Proper losses for learning from partial labels Jesús Cid-sueiro
Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation Benjamin Rolfs, Bala Rajaratnam, Dominique Guillot, Ian Wong, Arian Maleki
Selecting Diverse Features via Spectral Regularization Abhimanyu Das, Anirban Dasgupta, Ravi Kumar
Monte Carlo Methods for Maximum Margin Supervised Topic Models Qixia Jiang, Jun Zhu, Maosong Sun, Eric P. Xing
Parametric Local Metric Learning for Nearest Neighbor Classification Jun Wang, Alexandros Kalousis, Adam Woznica
A Linear Time Active Learning Algorithm for Link Classification Nicolò Cesa-bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella
Bayesian Warped Gaussian Processes Miguel Lázaro-Gredilla
Nonparametric Reduced Rank Regression Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty
Multiresolution analysis on the symmetric group Risi Kondor, Walter Dempsey
Isotropic Hashing Weihao Kong, Wu-jun Li
On Lifting the Gibbs Sampling Algorithm Deepak Venugopal, Vibhav Gogate
On the connections between saliency and tracking Vijay Mahadevan, Nuno Vasconcelos
Convex Multi-view Subspace Learning Martha White, Xinhua Zhang, Dale Schuurmans, Yao-liang Yu
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data Lars Buesing, Jakob H. Macke, Maneesh Sahani
Mixability in Statistical Learning Tim V. Erven, Peter Grünwald, Mark D. Reid, Robert C. Williamson
Waveform Driven Plasticity in BiFeO3 Memristive Devices: Model and Implementation Christian Mayr, Paul Stärke, Johannes Partzsch, Love Cederstroem, Rene Schüffny, Yao Shuai, Nan Du, Heidemarie Schmidt
A lattice filter model of the visual pathway Karol Gregor, Dmitri B. Chklovskii
Semantic Kernel Forests from Multiple Taxonomies Sung Ju Hwang, Kristen Grauman, Fei Sha
Causal discovery with scale-mixture model for spatiotemporal variance dependencies Zhitang Chen, Kun Zhang, Laiwan Chan
Natural Images, Gaussian Mixtures and Dead Leaves Daniel Zoran, Yair Weiss
Dual-Space Analysis of the Sparse Linear Model Yi Wu, David P. Wipf
Active Comparison of Prediction Models Christoph Sawade, Niels Landwehr, Tobias Scheffer
Online Regret Bounds for Undiscounted Continuous Reinforcement Learning Ronald Ortner, Daniil Ryabko
Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning Jinfeng Yi, Rong Jin, Shaili Jain, Tianbao Yang, Anil K. Jain
Learning curves for multi-task Gaussian process regression Peter Sollich, Simon Ashton
Kernel Hyperalignment Alexander Lorbert, Peter J. Ramadge
Multiple Choice Learning: Learning to Produce Multiple Structured Outputs Abner Guzmán-rivera, Dhruv Batra, Pushmeet Kohli
Mixing Properties of Conditional Markov Chains with Unbounded Feature Functions Mathieu Sinn, Bei Chen
Persistent Homology for Learning Densities with Bounded Support Florian T. Pokorny, Hedvig Kjellström, Danica Kragic, Carl Ek
On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes Bruno Scherrer, Boris Lesner
Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model Sander M. Bohte
MAP Inference in Chains using Column Generation David Belanger, Alexandre Passos, Sebastian Riedel, Andrew McCallum
Bayesian Nonparametric Modeling of Suicide Attempts Francisco Ruiz, Isabel Valera, Carlos Blanco, Fernando Pérez-Cruz
Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling Antonino Freno, Mikaela Keller, Marc Tommasi
Neurally Plausible Reinforcement Learning of Working Memory Tasks Jaldert Rombouts, Pieter Roelfsema, Sander M. Bohte
Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions Neil Burch, Marc Lanctot, Duane Szafron, Richard G. Gibson
Repulsive Mixtures Francesca Petralia, Vinayak Rao, David B. Dunson
Fully Bayesian inference for neural models with negative-binomial spiking James Scott, Jonathan W. Pillow
Slice Normalized Dynamic Markov Logic Networks Tivadar Papai, Henry Kautz, Daniel Stefankovic
Meta-Gaussian Information Bottleneck Melanie Rey, Volker Roth
Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification Yung-kyun Noh, Frank Park, Daniel D. Lee
The Perturbed Variation Maayan Harel, Shie Mannor
Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization Konstantinos Tsianos, Sean Lawlor, Michael G. Rabbat
The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes Simon Lyons, Amos J. Storkey, Simo Särkkä
Online allocation and homogeneous partitioning for piecewise constant mean-approximation Alexandra Carpentier, Odalric-ambrym Maillard
Learning as MAP Inference in Discrete Graphical Models Xianghang Liu, James Petterson, Tibério S. Caetano
A mechanistic model of early sensory processing based on subtracting sparse representations Shaul Druckmann, Tao Hu, Dmitri B. Chklovskii
Multi-Stage Multi-Task Feature Learning Pinghua Gong, Jieping Ye, Chang-shui Zhang
From Deformations to Parts: Motion-based Segmentation of 3D Objects Soumya Ghosh, Matthew Loper, Erik B. Sudderth, Michael J. Black
Phoneme Classification using Constrained Variational Gaussian Process Dynamical System Hyunsin Park, Sungrack Yun, Sanghyuk Park, Jongmin Kim, Chang D. Yoo
Bayesian estimation of discrete entropy with mixtures of stick-breaking priors Evan Archer, Il Memming Park, Jonathan W. Pillow
A Geometric take on Metric Learning Søren Hauberg, Oren Freifeld, Michael J. Black
Learning the Architecture of Sum-Product Networks Using Clustering on Variables Aaron Dennis, Dan Ventura
Pointwise Tracking the Optimal Regression Function Yair Wiener, Ran El-Yaniv
Bayesian nonparametric models for bipartite graphs Francois Caron
Reducing statistical time-series problems to binary classification Daniil Ryabko, Jeremie Mary
Tractable Objectives for Robust Policy Optimization Katherine Chen, Michael Bowling
Classification Calibration Dimension for General Multiclass Losses Harish G. Ramaswamy, Shivani Agarwal
Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses Po-ling Loh, Martin J. Wainwright
Collaborative Gaussian Processes for Preference Learning Neil Houlsby, Ferenc Huszar, Zoubin Ghahramani, Jose M. Hernández-lobato
Approximating Concavely Parameterized Optimization Problems Joachim Giesen, Jens Mueller, Soeren Laue, Sascha Swiercy
Gradient-based kernel method for feature extraction and variable selection Kenji Fukumizu, Chenlei Leng
Strategic Impatience in Go/NoGo versus Forced-Choice Decision-Making Pradeep Shenoy, Angela J. Yu
On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks Qirong Ho, Junming Yin, Eric P. Xing
Relax and Randomize : From Value to Algorithms Sasha Rakhlin, Ohad Shamir, Karthik Sridharan
Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL Nishant Mehta, Dongryeol Lee, Alexander G. Gray
Spectral Learning of General Weighted Automata via Constrained Matrix Completion Borja Balle, Mehryar Mohri
Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum L_p Loss Zhuo Wang, Alan A. Stocker, Daniel D. Lee
Algorithms for Learning Markov Field Policies Abdeslam Boularias, Jan R. Peters, Oliver B. Kroemer
Affine Independent Variational Inference Edward Challis, David Barber
Learning from the Wisdom of Crowds by Minimax Entropy Denny Zhou, Sumit Basu, Yi Mao, John C. Platt
Clustering Sparse Graphs Yudong Chen, Sujay Sanghavi, Huan Xu
Sketch-Based Linear Value Function Approximation Marc Bellemare, Joel Veness, Michael Bowling
Multimodal Learning with Deep Boltzmann Machines Nitish Srivastava, Ruslan R. Salakhutdinov
Learning with Target Prior Zuoguan Wang, Siwei Lyu, Gerwin Schalk, Qiang Ji
Slice sampling normalized kernel-weighted completely random measure mixture models Nicholas Foti, Sinead Williamson
Scalable Inference of Overlapping Communities Prem K. Gopalan, Sean Gerrish, Michael Freedman, David M. Blei, David M. Mimno
Online L1-Dictionary Learning with Application to Novel Document Detection Shiva P. Kasiviswanathan, Huahua Wang, Arindam Banerjee, Prem Melville
A systematic approach to extracting semantic information from functional MRI data Francisco Pereira, Matthew Botvinick
Why MCA? Nonlinear sparse coding with spike-and-slab prior for neurally plausible image encoding Philip Sterne, Joerg Bornschein, Abdul-saboor Sheikh, Joerg Luecke, Jacquelyn A. Shelton
Learning optimal spike-based representations Ralph Bourdoukan, David Barrett, Sophie Deneve, Christian K. Machens
Collaborative Ranking With 17 Parameters Maksims Volkovs, Richard S. Zemel
Rational inference of relative preferences Nisheeth Srivastava, Paul R. Schrater
The topographic unsupervised learning of natural sounds in the auditory cortex Hiroki Terashima, Masato Okada
Approximating Equilibria in Sequential Auctions with Incomplete Information and Multi-Unit Demand Amy Greenwald, Jiacui Li, Eric Sodomka
A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation Cho-jui Hsieh, Arindam Banerjee, Inderjit S. Dhillon, Pradeep K. Ravikumar
A Simple and Practical Algorithm for Differentially Private Data Release Moritz Hardt, Katrina Ligett, Frank Mcsherry
Bayesian active learning with localized priors for fast receptive field characterization Mijung Park, Jonathan W. Pillow
Weighted Likelihood Policy Search with Model Selection Tsuyoshi Ueno, Kohei Hayashi, Takashi Washio, Yoshinobu Kawahara
Learning the Dependency Structure of Latent Factors Yunlong He, Yanjun Qi, Koray Kavukcuoglu, Haesun Park
Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva
Globally Convergent Dual MAP LP Relaxation Solvers using Fenchel-Young Margins Alex Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun
Dip-means: an incremental clustering method for estimating the number of clusters Argyris Kalogeratos, Aristidis Likas
No-Regret Algorithms for Unconstrained Online Convex Optimization Brendan Mcmahan, Matthew Streeter
Bayesian models for Large-scale Hierarchical Classification Siddharth Gopal, Yiming Yang, Bing Bai, Alexandru Niculescu-mizil
Recovery of Sparse Probability Measures via Convex Programming Mert Pilanci, Laurent E. Ghaoui, Venkat Chandrasekaran
Multiple Operator-valued Kernel Learning Hachem Kadri, Alain Rakotomamonjy, Philippe Preux, Francis R. Bach
Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning Ulugbek Kamilov, Sundeep Rangan, Michael Unser, Alyson K. Fletcher
A Better Way to Pretrain Deep Boltzmann Machines Geoffrey E. Hinton, Ruslan R. Salakhutdinov
Towards a learning-theoretic analysis of spike-timing dependent plasticity David Balduzzi, Michel Besserve
Learning Manifolds with K-Means and K-Flats Guillermo Canas, Tomaso Poggio, Lorenzo Rosasco
Iterative ranking from pair-wise comparisons Sahand Negahban, Sewoong Oh, Devavrat Shah
A Polynomial-time Form of Robust Regression Yao-liang Yu, Özlem Aslan, Dale Schuurmans
Learning Probability Measures with respect to Optimal Transport Metrics Guillermo Canas, Lorenzo Rosasco
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models Weiwei Cheng, Eyke Hüllermeier, Willem Waegeman, Volkmar Welker
Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting Amadou Ba, Mathieu Sinn, Yannig Goude, Pascal Pompey
Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs Michael Collins, Shay B. Cohen
Semi-supervised Eigenvectors for Locally-biased Learning Toke Hansen, Michael W. Mahoney
Exponential Concentration for Mutual Information Estimation with Application to Forests Han Liu, Larry Wasserman, John D. Lafferty
Augment-and-Conquer Negative Binomial Processes Mingyuan Zhou, Lawrence Carin
Transferring Expectations in Model-based Reinforcement Learning Trung Nguyen, Tomi Silander, Tze Y. Leong
Minimization of Continuous Bethe Approximations: A Positive Variation Jason Pacheco, Erik B. Sudderth
Non-linear Metric Learning Dor Kedem, Stephen Tyree, Fei Sha, Gert R. Lanckriet, Kilian Q. Weinberger
Factorial LDA: Sparse Multi-Dimensional Text Models Michael Paul, Mark Dredze
Ancestor Sampling for Particle Gibbs Fredrik Lindsten, Thomas Schön, Michael I. Jordan
Modelling Reciprocating Relationships with Hawkes Processes Charles Blundell, Jeff Beck, Katherine A. Heller
Expectation Propagation in Gaussian Process Dynamical Systems Marc Deisenroth, Shakir Mohamed
A quasi-Newton proximal splitting method Stephen Becker, Jalal Fadili
Exact and Stable Recovery of Sequences of Signals with Sparse Increments via Differential _1-Minimization Demba Ba, Behtash Babadi, Patrick Purdon, Emery Brown
Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems Morteza Ibrahimi, Adel Javanmard, Benjamin V. Roy
Multilabel Classification using Bayesian Compressed Sensing Ashish Kapoor, Raajay Viswanathan, Prateek Jain
Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization Stephen Bach, Matthias Broecheler, Lise Getoor, Dianne O'leary
A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets Nicolas L. Roux, Mark Schmidt, Francis R. Bach
Query Complexity of Derivative-Free Optimization Kevin G. Jamieson, Robert Nowak, Ben Recht
Emergence of Object-Selective Features in Unsupervised Feature Learning Adam Coates, Andrej Karpathy, Andrew Y. Ng
Burn-in, bias, and the rationality of anchoring Falk Lieder, Thomas Griffiths, Noah Goodman
Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes Michael Bryant, Erik B. Sudderth
A Neural Autoregressive Topic Model Hugo Larochelle, Stanislas Lauly
A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes Thomas Furmston, David Barber
Entangled Monte Carlo Seong-hwan Jun, Liangliang Wang, Alexandre Bouchard-côté
Near-Optimal MAP Inference for Determinantal Point Processes Jennifer Gillenwater, Alex Kulesza, Ben Taskar
Probabilistic Low-Rank Subspace Clustering S. D. Babacan, Shinichi Nakajima, Minh Do
How They Vote: Issue-Adjusted Models of Legislative Behavior Sean Gerrish, David M. Blei
Density Propagation and Improved Bounds on the Partition Function Stefano Ermon, Ashish Sabharwal, Bart Selman, Carla P. Gomes
Perceptron Learning of SAT Alex Flint, Matthew Blaschko
Learning Networks of Heterogeneous Influence Nan Du, Le Song, Ming Yuan, Alex J. Smola
Multiclass Learning with Simplex Coding Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco, Jean-jeacques Slotine
FastEx: Hash Clustering with Exponential Families Amr Ahmed, Sujith Ravi, Alex J. Smola, Shravan M. Narayanamurthy
Topic-Partitioned Multinetwork Embeddings Peter Krafft, Juston Moore, Bruce Desmarais, Hanna M. Wallach
Learning Label Trees for Probabilistic Modelling of Implicit Feedback Andriy Mnih, Yee W. Teh
Learning with Recursive Perceptual Representations Oriol Vinyals, Yangqing Jia, Li Deng, Trevor Darrell
Link Prediction in Graphs with Autoregressive Features Emile Richard, Stephane Gaiffas, Nicolas Vayatis
Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images Dan Ciresan, Alessandro Giusti, Luca M. Gambardella, Juergen Schmidhuber
Scalable imputation of genetic data with a discrete fragmentation-coagulation process Lloyd Elliott, Yee W. Teh
Gradient Weights help Nonparametric Regressors Samory Kpotufe, Abdeslam Boularias
Online Sum-Product Computation Over Trees Mark Herbster, Stephen Pasteris, Fabio Vitale
Sparse Approximate Manifolds for Differential Geometric MCMC Ben Calderhead, Mátyás A. Sustik
Fast Variational Inference in the Conjugate Exponential Family James Hensman, Magnus Rattray, Neil D. Lawrence
Bayesian Pedigree Analysis using Measure Factorization Bonnie Kirkpatrick, Alexandre Bouchard-côté
Accelerated Training for Matrix-norm Regularization: A Boosting Approach Xinhua Zhang, Dale Schuurmans, Yao-liang Yu
Controlled Recognition Bounds for Visual Learning and Exploration Vasiliy Karasev, Alessandro Chiuso, Stefano Soatto
Distributed Probabilistic Learning for Camera Networks with Missing Data Sejong Yoon, Vladimir Pavlovic
Submodular-Bregman and the Lovász-Bregman Divergences with Applications Rishabh Iyer, Jeff A. Bilmes
Minimizing Uncertainty in Pipelines Nilesh Dalvi, Aditya Parameswaran, Vibhor Rastogi
Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek, Hugo Larochelle, Ryan P. Adams
Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach Dijun Luo, Heng Huang, Feiping Nie, Chris H. Ding
The Time-Marginalized Coalescent Prior for Hierarchical Clustering Levi Boyles, Max Welling
Fusion with Diffusion for Robust Visual Tracking Yu Zhou, Xiang Bai, Wenyu Liu, Longin J. Latecki
A nonparametric variable clustering model Konstantina Palla, Zoubin Ghahramani, David A. Knowles
Priors for Diversity in Generative Latent Variable Models James T. Kwok, Ryan P. Adams
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function Pedro Ortega, Jordi Grau-moya, Tim Genewein, David Balduzzi, Daniel Braun
Convergence Rate Analysis of MAP Coordinate Minimization Algorithms Ofer Meshi, Amir Globerson, Tommi S. Jaakkola
Projection Retrieval for Classification Madalina Fiterau, Artur Dubrawski
Hierarchical spike coding of sound Yan Karklin, Chaitanya Ekanadham, Eero P. Simoncelli
Human memory search as a random walk in a semantic network Joseph L. Austerweil, Joshua T. Abbott, Thomas L. Griffiths
Probabilistic n-Choose-k Models for Classification and Ranking Kevin Swersky, Brendan J. Frey, Daniel Tarlow, Richard S. Zemel, Ryan P. Adams
Complex Inference in Neural Circuits with Probabilistic Population Codes and Topic Models Jeff Beck, Alexandre Pouget, Katherine A. Heller
Cost-Sensitive Exploration in Bayesian Reinforcement Learning Dongho Kim, Kee-eung Kim, Pascal Poupart
Learning with Partially Absorbing Random Walks Xiao-ming Wu, Zhenguo Li, Anthony M. So, John Wright, Shih-fu Chang
Locating Changes in Highly Dependent Data with Unknown Number of Change Points Azadeh Khaleghi, Daniil Ryabko
Probabilistic Event Cascades for Alzheimer's disease Jonathan Huang, Daniel Alexander
Efficient and direct estimation of a neural subunit model for sensory coding Brett Vintch, Andrew Zaharia, J Movshon, Eero P. Simoncelli
One Permutation Hashing Ping Li, Art Owen, Cun-hui Zhang
Unsupervised Template Learning for Fine-Grained Object Recognition Shulin Yang, Liefeng Bo, Jue Wang, Linda G. Shapiro
Risk Aversion in Markov Decision Processes via Near Optimal Chernoff Bounds Teodor M. Moldovan, Pieter Abbeel
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression Emtiyaz Khan, Shakir Mohamed, Kevin P. Murphy
Imitation Learning by Coaching He He, Jason Eisner, Hal Daume
Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models Ke Jiang, Brian Kulis, Michael I. Jordan
A latent factor model for highly multi-relational data Rodolphe Jenatton, Nicolas L. Roux, Antoine Bordes, Guillaume R. Obozinski
Entropy Estimations Using Correlated Symmetric Stable Random Projections Ping Li, Cun-hui Zhang
Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression Piyush Rai, Abhishek Kumar, Hal Daume
Continuous Relaxations for Discrete Hamiltonian Monte Carlo Yichuan Zhang, Zoubin Ghahramani, Amos J. Storkey, Charles A. Sutton
Deep Learning of Invariant Features via Simulated Fixations in Video Will Zou, Shenghuo Zhu, Kai Yu, Andrew Y. Ng
Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric
On the Sample Complexity of Robust PCA Matthew Coudron, Gilad Lerman
Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning Matthew Der, Lawrence K. Saul
Discriminative Learning of Sum-Product Networks Robert Gens, Pedro Domingos
Trajectory-Based Short-Sighted Probabilistic Planning Felipe Trevizan, Manuela Veloso
Tight Bounds on Profile Redundancy and Distinguishability Jayadev Acharya, Hirakendu Das, Alon Orlitsky
Interpreting prediction markets: a stochastic approach Rafael M. Frongillo, Nicholás Della Penna, Mark D. Reid
Risk-Aversion in Multi-armed Bandits Amir Sani, Alessandro Lazaric, Rémi Munos
Confusion-Based Online Learning and a Passive-Aggressive Scheme Liva Ralaivola
Cardinality Restricted Boltzmann Machines Kevin Swersky, Ilya Sutskever, Daniel Tarlow, Richard S. Zemel, Ruslan R. Salakhutdinov, Ryan P. Adams
Generalization Bounds for Domain Adaptation Chao Zhang, Lei Zhang, Jieping Ye

Advances in Neural Information Processing Systems 26 (NIPS 2013)

The papers below appear in Advances in Neural Information Processing Systems 26 edited by C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger .
They are proceedings from the conference, "Neural Information Processing Systems 2013."

The Randomized Dependence Coefficient David Lopez-Paz, Philipp Hennig, Prof. Bernhard Schölkopf
Documents as multiple overlapping windows into grids of counts Alessandro Perina, Nebojsa Jojic, Manuele Bicego, Andrzej Truski
Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively Wen-Hao Zhang, Si Wu
Latent Maximum Margin Clustering Guang-Tong Zhou, Tian Lan, Arash Vahdat, Greg Mori
Data-driven Distributionally Robust Polynomial Optimization Martin Mevissen, Emanuele Ragnoli, Jia Yuan Yu
Transfer Learning in a Transductive Setting Marcus Rohrbach, Sandra Ebert, Bernt Schiele
Bayesian optimization explains human active search Ali Borji, Laurent Itti
Provable Subspace Clustering: When LRR meets SSC Yu-Xiang Wang, Huan Xu, Chenlei Leng
Generalized Random Utility Models with Multiple Types Hossein Azari Soufiani, Hansheng Diao, Zhenyu Lai, David C. Parkes
Polar Operators for Structured Sparse Estimation Xinhua Zhang, Yao-Liang Yu, Dale Schuurmans
On Decomposing the Proximal Map Yao-Liang Yu
Point Based Value Iteration with Optimal Belief Compression for Dec-POMDPs Liam C. MacDermed, Charles Isbell
PAC-Bayes-Empirical-Bernstein Inequality Ilya O. Tolstikhin, Yevgeny Seldin
Modeling Clutter Perception using Parametric Proto-object Partitioning Chen-Ping Yu, Wen-Yu Hua, Dimitris Samaras, Greg Zelinsky
Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching Marcelo Fiori, Pablo Sprechmann, Joshua Vogelstein, Pablo Muse, Guillermo Sapiro
Transportability from Multiple Environments with Limited Experiments Elias Bareinboim, Sanghack Lee, Vasant Honavar, Judea Pearl
More data speeds up training time in learning halfspaces over sparse vectors Amit Daniely, Nati Linial, Shai Shalev-Shwartz
Causal Inference on Time Series using Restricted Structural Equation Models Jonas Peters, Dominik Janzing, Prof. Bernhard Schölkopf
Deep Fisher Networks for Large-Scale Image Classification Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
Sparse Additive Text Models with Low Rank Background Lei Shi
Variance Reduction for Stochastic Gradient Optimization Chong Wang, Xi Chen, Alexander J. Smola, Eric P. Xing
Training and Analysing Deep Recurrent Neural Networks Michiel Hermans, Benjamin Schrauwen
A simple example of Dirichlet process mixture inconsistency for the number of components Jeffrey W. Miller, Matthew T. Harrison
Variational Policy Search via Trajectory Optimization Sergey Levine, Vladlen Koltun
Scalable kernels for graphs with continuous attributes Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten Borgwardt
Density estimation from unweighted k-nearest neighbor graphs: a roadmap Ulrike Von Luxburg, Morteza Alamgir
Decision Jungles: Compact and Rich Models for Classification Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, Antonio Criminisi
What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach Zhenwen Dai, Georgios Exarchakis, Jörg Lücke
Actor-Critic Algorithms for Risk-Sensitive MDPs Prashanth L.A., Mohammad Ghavamzadeh
Summary Statistics for Partitionings and Feature Allocations Isik B. Fidaner, Taylan Cemgil
One-shot learning and big data with n=2 Lee H. Dicker, Dean P. Foster
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression Michalis Titsias RC AUEB, Miguel Lazaro-Gredilla
Correlations strike back (again): the case of associative memory retrieval Cristina Savin, Peter Dayan, Mate Lengyel
Optimal Neural Population Codes for High-dimensional Stimulus Variables Zhuo Wang, Alan A. Stocker, Daniel D. Lee
Online Variational Approximations to non-Exponential Family Change Point Models: With Application to Radar Tracking Ryan D. Turner, Steven Bottone, Clay J. Stanek
Accelerating Stochastic Gradient Descent using Predictive Variance Reduction Rie Johnson, Tong Zhang
Using multiple samples to learn mixture models Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana
Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition Tzu-Kuo Huang, Jeff Schneider
On model selection consistency of penalized M-estimators: a geometric theory Jason D. Lee, Yuekai Sun, Jonathan E. Taylor
Dropout Training as Adaptive Regularization Stefan Wager, Sida Wang, Percy S. Liang
New Subsampling Algorithms for Fast Least Squares Regression Paramveer Dhillon, Yichao Lu, Dean P. Foster, Lyle Ungar
Faster Ridge Regression via the Subsampled Randomized Hadamard Transform Yichao Lu, Paramveer Dhillon, Dean P. Foster, Lyle Ungar
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent Shai Shalev-Shwartz, Tong Zhang
Improved and Generalized Upper Bounds on the Complexity of Policy Iteration Bruno Scherrer
Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation Dahua Lin
Online Robust PCA via Stochastic Optimization Jiashi Feng, Huan Xu, Shuicheng Yan
Least Informative Dimensions Fabian Sinz, Anna Stockl, Jan Grewe, Jan Benda
A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks Junming Yin, Qirong Ho, Eric P. Xing
Understanding variable importances in forests of randomized trees Gilles Louppe, Louis Wehenkel, Antonio Sutera, Pierre Geurts
Correlated random features for fast semi-supervised learning Brian McWilliams, David Balduzzi, Joachim M. Buhmann
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin
Better Approximation and Faster Algorithm Using the Proximal Average Yao-Liang Yu
Rapid Distance-Based Outlier Detection via Sampling Mahito Sugiyama, Karsten Borgwardt
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima Po-Ling Loh, Martin J. Wainwright
Non-Linear Domain Adaptation with Boosting Carlos J. Becker, Christos M. Christoudias, Pascal Fua
Mid-level Visual Element Discovery as Discriminative Mode Seeking Carl Doersch, Abhinav Gupta, Alexei A. Efros
q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions Assaf Glazer, Michael Lindenbaum, Shaul Markovitch
Auditing: Active Learning with Outcome-Dependent Query Costs Sivan Sabato, Anand D. Sarwate, Nati Srebro
A message-passing algorithm for multi-agent trajectory planning Jose Bento, Nate Derbinsky, Javier Alonso-Mora, Jonathan S. Yedidia
Learning Stochastic Feedforward Neural Networks Yichuan Tang, Ruslan R. Salakhutdinov
Inferring neural population dynamics from multiple partial recordings of the same neural circuit Srini Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Hausser, Jakob Macke
Multi-Prediction Deep Boltzmann Machines Ian Goodfellow, Mehdi Mirza, Aaron Courville, Yoshua Bengio
Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation Vibhav Vineet, Carsten Rother, Philip Torr
Blind Calibration in Compressed Sensing using Message Passing Algorithms Christophe Schulke, Francesco Caltagirone, Florent Krzakala, Lenka Zdeborová
Learning Trajectory Preferences for Manipulators via Iterative Improvement Ashesh Jain, Brian Wojcik, Thorsten Joachims, Ashutosh Saxena
Large Scale Distributed Sparse Precision Estimation Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep K. Ravikumar, Inderjit S. Dhillon
Neural representation of action sequences: how far can a simple snippet-matching model take us? Cheston Tan, Jedediah M. Singer, Thomas Serre, David Sheinberg, Tomaso Poggio
On Algorithms for Sparse Multi-factor NMF Siwei Lyu, Xin Wang
Dirty Statistical Models Eunho Yang, Pradeep K. Ravikumar
Parallel Sampling of DP Mixture Models using Sub-Cluster Splits Jason Chang, John W. Fisher III
Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent Tianbao Yang
Prior-free and prior-dependent regret bounds for Thompson Sampling Sebastien Bubeck, Che-Yu Liu
Structured Learning via Logistic Regression Justin Domke
Which Space Partitioning Tree to Use for Search? Parikshit Ram, Alexander Gray
Projecting Ising Model Parameters for Fast Mixing Justin Domke, Xianghang Liu
Mixed Optimization for Smooth Functions Mehrdad Mahdavi, Lijun Zhang, Rong Jin
Conditional Random Fields via Univariate Exponential Families Eunho Yang, Pradeep K. Ravikumar, Genevera I. Allen, Zhandong Liu
Stochastic blockmodel approximation of a graphon: Theory and consistent estimation Edo M. Airoldi, Thiago B. Costa, Stanley H. Chan
Reinforcement Learning in Robust Markov Decision Processes Shiau Hong Lim, Huan Xu, Shie Mannor
On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization Ke Hou, Zirui Zhou, Anthony Man-Cho So, Zhi-Quan Luo
Recurrent networks of coupled Winner-Take-All oscillators for solving constraint satisfaction problems Hesham Mostafa, Lorenz. K. Mueller, Giacomo Indiveri
Latent Structured Active Learning Wenjie Luo, Alex Schwing, Raquel Urtasun
A Gang of Bandits Nicolò Cesa-Bianchi, Claudio Gentile, Giovanni Zappella
Learning Feature Selection Dependencies in Multi-task Learning Daniel Hernández-Lobato, José Miguel Hernández-Lobato
B-test: A Non-parametric, Low Variance Kernel Two-sample Test Wojciech Zaremba, Arthur Gretton, Matthew Blaschko
Online PCA for Contaminated Data Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) Francis Bach, Eric Moulines
Efficient Algorithm for Privately Releasing Smooth Queries Ziteng Wang, Kai Fan, Jiaqi Zhang, Liwei Wang
Beyond Pairwise: Provably Fast Algorithms for Approximate k-Way Similarity Search Anshumali Shrivastava, Ping Li
Unsupervised Spectral Learning of Finite State Transducers Raphael Bailly, Xavier Carreras, Ariadna Quattoni
Learning a Deep Compact Image Representation for Visual Tracking Naiyan Wang, Dit-Yan Yeung
Learning Multi-level Sparse Representations Ferran Diego Andilla, Fred A. Hamprecht
Robust Data-Driven Dynamic Programming Grani Adiwena Hanasusanto, Daniel Kuhn
Low-Rank Matrix and Tensor Completion via Adaptive Sampling Akshay Krishnamurthy, Aarti Singh
Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms Adrien Todeschini, François Caron, Marie Chavent
Distributed Exploration in Multi-Armed Bandits Eshcar Hillel, Zohar S. Karnin, Tomer Koren, Ronny Lempel, Oren Somekh
The Pareto Regret Frontier Wouter M. Koolen
Direct 0-1 Loss Minimization and Margin Maximization with Boosting Shaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang
Regret based Robust Solutions for Uncertain Markov Decision Processes Asrar Ahmed, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet
Speeding up Permutation Testing in Neuroimaging Chris Hinrichs, Vamsi K. Ithapu, Qinyuan Sun, Sterling C. Johnson, Vikas Singh
Generalized Denoising Auto-Encoders as Generative Models Yoshua Bengio, Li Yao, Guillaume Alain, Pascal Vincent
Supervised Sparse Analysis and Synthesis Operators Pablo Sprechmann, Roee Litman, Tal Ben Yakar, Alexander M. Bronstein, Guillermo Sapiro
Low-rank matrix reconstruction and clustering via approximate message passing Ryosuke Matsushita, Toshiyuki Tanaka
Reasoning With Neural Tensor Networks for Knowledge Base Completion Richard Socher, Danqi Chen, Christopher D. Manning, Andrew Ng
Zero-Shot Learning Through Cross-Modal Transfer Richard Socher, Milind Ganjoo, Christopher D. Manning, Andrew Ng
Estimating LASSO Risk and Noise Level Mohsen Bayati, Murat A. Erdogdu, Andrea Montanari
Learning Adaptive Value of Information for Structured Prediction David J. Weiss, Ben Taskar
Efficient Online Inference for Bayesian Nonparametric Relational Models Dae Il Kim, Prem K. Gopalan, David Blei, Erik Sudderth
Approximate inference in latent Gaussian-Markov models from continuous time observations Botond Cseke, Manfred Opper, Guido Sanguinetti
Linear Convergence with Condition Number Independent Access of Full Gradients Lijun Zhang, Mehrdad Mahdavi, Rong Jin
When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements Divyanshu Vats, Richard Baraniuk
Wavelets on Graphs via Deep Learning Raif Rustamov, Leonidas J. Guibas
Robust Spatial Filtering with Beta Divergence Wojciech Samek, Duncan Blythe, Klaus-Robert Müller, Motoaki Kawanabe
Convex Relaxations for Permutation Problems Fajwel Fogel, Rodolphe Jenatton, Francis Bach, Alexandre D'Aspremont
High-Dimensional Gaussian Process Bandits Josip Djolonga, Andreas Krause, Volkan Cevher
A memory frontier for complex synapses Subhaneil Lahiri, Surya Ganguli
Marginals-to-Models Reducibility Tim Roughgarden, Michael Kearns
First-order Decomposition Trees Nima Taghipour, Jesse Davis, Hendrik Blockeel
A Comparative Framework for Preconditioned Lasso Algorithms Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jordan
Lasso Screening Rules via Dual Polytope Projection Jie Wang, Jiayu Zhou, Peter Wonka, Jieping Ye
Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent Yuening Hu, Jordan L. Boyd-Graber, Hal Daume III, Z. Irene Ying
A Latent Source Model for Nonparametric Time Series Classification George H. Chen, Stanislav Nikolov, Devavrat Shah
Efficient Optimization for Sparse Gaussian Process Regression Yanshuai Cao, Marcus A. Brubaker, David J. Fleet, Aaron Hertzmann
Lexical and Hierarchical Topic Regression Viet-An Nguyen, Jordan L. Boyd-Graber, Philip Resnik
Stochastic Convex Optimization with Multiple Objectives Mehrdad Mahdavi, Tianbao Yang, Rong Jin
A Kernel Test for Three-Variable Interactions Dino Sejdinovic, Arthur Gretton, Wicher Bergsma
Memoized Online Variational Inference for Dirichlet Process Mixture Models Michael C. Hughes, Erik Sudderth
Designed Measurements for Vector Count Data Liming Wang, David E. Carlson, Miguel Rodrigues, David Wilcox, Robert Calderbank, Lawrence Carin
Robust Transfer Principal Component Analysis with Rank Constraints Yuhong Guo
Online Learning with Switching Costs and Other Adaptive Adversaries Nicolò Cesa-Bianchi, Ofer Dekel, Ohad Shamir
Learning Prices for Repeated Auctions with Strategic Buyers Kareem Amin, Afshin Rostamizadeh, Umar Syed
Probabilistic Principal Geodesic Analysis Miaomiao Zhang, P.T. Fletcher
Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models Adel Javanmard, Andrea Montanari
Learning with Noisy Labels Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep K. Ravikumar, Ambuj Tewari
Tracking Time-varying Graphical Structure Erich Kummerfeld, David Danks
Factorized Asymptotic Bayesian Inference for Latent Feature Models Kohei Hayashi, Ryohei Fujimaki
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B. Gibbons, Garth A. Gibson, Greg Ganger, Eric P. Xing
Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models Jie Liu, David Page
Online Learning with Costly Features and Labels Navid Zolghadr, Gabor Bartok, Russell Greiner, András György, Csaba Szepesvari
Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions Eftychios A. Pnevmatikakis, Liam Paninski
A Novel Two-Step Method for Cross Language Representation Learning Min Xiao, Yuhong Guo
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations Tamir Hazan, Subhransu Maji, Tommi Jaakkola
Graphical Models for Inference with Missing Data Karthika Mohan, Judea Pearl, Jin Tian
Reshaping Visual Datasets for Domain Adaptation Boqing Gong, Kristen Grauman, Fei Sha
Statistical Active Learning Algorithms Maria-Florina F. Balcan, Vitaly Feldman
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits Ben Shababo, Brooks Paige, Ari Pakman, Liam Paninski
Reflection methods for user-friendly submodular optimization Stefanie Jegelka, Francis Bach, Suvrit Sra
Unsupervised Structure Learning of Stochastic And-Or Grammars Kewei Tu, Maria Pavlovskaia, Song-Chun Zhu
Convex Tensor Decomposition via Structured Schatten Norm Regularization Ryota Tomioka, Taiji Suzuki
Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs Yann Dauphin, Yoshua Bengio
Learning Chordal Markov Networks by Constraint Satisfaction Jukka Corander, Tomi Janhunen, Jussi Rintanen, Henrik Nyman, Johan Pensar
Parametric Task Learning Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima
A Deep Architecture for Matching Short Texts Zhengdong Lu, Hang Li
Computing the Stationary Distribution Locally Christina E. Lee, Asuman Ozdaglar, Devavrat Shah
Nonparametric Multi-group Membership Model for Dynamic Networks Myunghwan Kim, Jure Leskovec
Adaptive Step-Size for Policy Gradient Methods Matteo Pirotta, Marcello Restelli, Luca Bascetta
Optimistic Concurrency Control for Distributed Unsupervised Learning Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan
Reservoir Boosting : Between Online and Offline Ensemble Learning Leonidas Lefakis, François Fleuret
Multiclass Total Variation Clustering Xavier Bresson, Thomas Laurent, David Uminsky, James von Brecht
Approximate Inference in Continuous Determinantal Processes Raja Hafiz Affandi, Emily Fox, Ben Taskar
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi
Thompson Sampling for 1-Dimensional Exponential Family Bandits Nathaniel Korda, Emilie Kaufmann, Remi Munos
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion Nguyen Viet Cuong, Wee Sun Lee, Nan Ye, Kian Ming A. Chai, Hai Leong Chieu
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals Barbara Rakitsch, Christoph Lippert, Karsten Borgwardt, Oliver Stegle
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses Harish G. Ramaswamy, Shivani Agarwal, Ambuj Tewari
Inverse Density as an Inverse Problem: the Fredholm Equation Approach Qichao Que, Mikhail Belkin
Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising Forest Agostinelli, Michael R. Anderson, Honglak Lee
EDML for Learning Parameters in Directed and Undirected Graphical Models Khaled S. Refaat, Arthur Choi, Adnan Darwiche
Similarity Component Analysis Soravit Changpinyo, Kuan Liu, Fei Sha
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Josh Tenenbaum
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation John Duchi, Martin J. Wainwright, Michael I. Jordan
Firing rate predictions in optimal balanced networks David G. Barrett, Sophie Denève, Christian K. Machens
Manifold-based Similarity Adaptation for Label Propagation Masayuki Karasuyama, Hiroshi Mamitsuka
Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty Haichao Zhang, David Wipf
Near-Optimal Entrywise Sampling for Data Matrices Dimitris Achlioptas, Zohar S. Karnin, Edo Liberty
Learning to Prune in Metric and Non-Metric Spaces Leonid Boytsov, Bilegsaikhan Naidan
Online learning in episodic Markovian decision processes by relative entropy policy search Alexander Zimin, Gergely Neu
Optimistic policy iteration and natural actor-critic: A unifying view and a non-optimality result Paul Wagner
Bayesian Hierarchical Community Discovery Charles Blundell, Yee Whye Teh
From Bandits to Experts: A Tale of Domination and Independence Noga Alon, Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour
Predictive PAC Learning and Process Decompositions Cosma Shalizi, Aryeh Kontorovich
Pass-efficient unsupervised feature selection Crystal Maung, Haim Schweitzer
Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors Xiaoqin Zhang, Di Wang, Zhengyuan Zhou, Yi Ma
Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search Aijun Bai, Feng Wu, Xiaoping Chen
Solving inverse problem of Markov chain with partial observations Tetsuro Morimura, Takayuki Osogami, Tsuyoshi Ide
Locally Adaptive Bayesian Multivariate Time Series Daniele Durante, Bruno Scarpa, David B. Dunson
Mapping paradigm ontologies to and from the brain Yannick Schwartz, Bertrand Thirion, Gael Varoquaux
Noise-Enhanced Associative Memories Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi, Lav R. Varshney
Exact and Stable Recovery of Pairwise Interaction Tensors Shouyuan Chen, Michael R. Lyu, Irwin King, Zenglin Xu
Bayesian entropy estimation for binary spike train data using parametric prior knowledge Evan W. Archer, Il Memming Park, Jonathan W. Pillow
Perfect Associative Learning with Spike-Timing-Dependent Plasticity Christian Albers, Maren Westkott, Klaus Pawelzik
On Poisson Graphical Models Eunho Yang, Pradeep K. Ravikumar, Genevera I. Allen, Zhandong Liu
Streaming Variational Bayes Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan
Gaussian Process Conditional Copulas with Applications to Financial Time Series José Miguel Hernández-Lobato, James R. Lloyd, Daniel Hernández-Lobato
Extracting regions of interest from biological images with convolutional sparse block coding Marius Pachitariu, Adam M. Packer, Noah Pettit, Henry Dalgleish, Michael Hausser, Maneesh Sahani
Approximate Dynamic Programming Finally Performs Well in the Game of Tetris Victor Gabillon, Mohammad Ghavamzadeh, Bruno Scherrer
Third-Order Edge Statistics: Contour Continuation, Curvature, and Cortical Connections Matthew Lawlor, Steven W. Zucker
DESPOT: Online POMDP Planning with Regularization Adhiraj Somani, Nan Ye, David Hsu, Wee Sun Lee
Matrix Completion From any Given Set of Observations Troy Lee, Adi Shraibman
Regression-tree Tuning in a Streaming Setting Samory Kpotufe, Francesco Orabona
Multiscale Dictionary Learning for Estimating Conditional Distributions Francesca Petralia, Joshua T. Vogelstein, David B. Dunson
Dimension-Free Exponentiated Gradient Francesco Orabona
Stochastic Optimization of PCA with Capped MSG Raman Arora, Andy Cotter, Nati Srebro
On Flat versus Hierarchical Classification in Large-Scale Taxonomies Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini
Learning Gaussian Graphical Models with Observed or Latent FVSs Ying Liu, Alan Willsky
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies Yangqing Jia, Joshua T. Abbott, Joseph L. Austerweil, Thomas Griffiths, Trevor Darrell
Robust Bloom Filters for Large MultiLabel Classification Tasks Moustapha M. Cisse, Nicolas Usunier, Thierry Artières, Patrick Gallinari
Solving the multi-way matching problem by permutation synchronization Deepti Pachauri, Risi Kondor, Vikas Singh
Generalizing Analytic Shrinkage for Arbitrary Covariance Structures Daniel Bartz, Klaus-Robert Müller
Top-Down Regularization of Deep Belief Networks Hanlin Goh, Nicolas Thome, Matthieu Cord, Joo-Hwee Lim
Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions Tamir Hazan, Subhransu Maji, Joseph Keshet, Tommi Jaakkola
Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms Yu Zhang
Machine Teaching for Bayesian Learners in the Exponential Family Xiaojin Zhu
Scoring Workers in Crowdsourcing: How Many Control Questions are Enough? Qiang Liu, Alexander T. Ihler, Mark Steyvers
Action from Still Image Dataset and Inverse Optimal Control to Learn Task Specific Visual Scanpaths Stefan Mathe, Cristian Sminchisescu
A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data Jasper Snoek, Richard Zemel, Ryan P. Adams
Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model Fang Han, Han Liu
Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation Bogdan Savchynskyy, Jörg Hendrik Kappes, Paul Swoboda, Christoph Schnörr
Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic James L. Sharpnack, Akshay Krishnamurthy, Aarti Singh
Demixing odors - fast inference in olfaction Agnieszka Grabska-Barwinska, Jeff Beck, Alexandre Pouget, Peter Latham
Learning Multiple Models via Regularized Weighting Daniel Vainsencher, Shie Mannor, Huan Xu
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity Anima Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade
Distributed k-means and k-median Clustering on General Topologies Maria-Florina F. Balcan, Steven Ehrlich, Yingyu Liang
Multi-Task Bayesian Optimization Kevin Swersky, Jasper Snoek, Ryan P. Adams
Online Learning of Dynamic Parameters in Social Networks Shahin Shahrampour, Sasha Rakhlin, Ali Jadbabaie
A Graphical Transformation for Belief Propagation: Maximum Weight Matchings and Odd-Sized Cycles Jinwoo Shin, Andrew E. Gelfand, Misha Chertkov
Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space Xinhua Zhang, Wee Sun Lee, Yee Whye Teh
Approximate Gaussian process inference for the drift function in stochastic differential equations Andreas Ruttor, Philipp Batz, Manfred Opper
Distributed Submodular Maximization: Identifying Representative Elements in Massive Data Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause
Adaptive Market Making via Online Learning Jacob Abernethy, Satyen Kale
On the Sample Complexity of Subspace Learning Alessandro Rudi, Guillermo D. Canas, Lorenzo Rosasco
Spike train entropy-rate estimation using hierarchical Dirichlet process priors Karin C. Knudson, Jonathan W. Pillow
Embed and Project: Discrete Sampling with Universal Hashing Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
Discriminative Transfer Learning with Tree-based Priors Nitish Srivastava, Ruslan R. Salakhutdinov
Small-Variance Asymptotics for Hidden Markov Models Anirban Roychowdhury, Ke Jiang, Brian Kulis
Convergence of Monte Carlo Tree Search in Simultaneous Move Games Viliam Lisy, Vojta Kovarik, Marc Lanctot, Branislav Bosansky
DeViSE: A Deep Visual-Semantic Embedding Model Andrea Frome, Greg S. Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, Tomas Mikolov
Reward Mapping for Transfer in Long-Lived Agents Xiaoxiao Guo, Satinder Singh, Richard L. Lewis
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation Martin Azizyan, Aarti Singh, Larry Wasserman
Predicting Parameters in Deep Learning Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas
Estimating the Unseen: Improved Estimators for Entropy and other Properties Paul Valiant, Gregory Valiant
What do row and column marginals reveal about your dataset? Behzad Golshan, John Byers, Evimaria Terzi
RNADE: The real-valued neural autoregressive density-estimator Benigno Uria, Iain Murray, Hugo Larochelle
Two-Target Algorithms for Infinite-Armed Bandits with Bernoulli Rewards Thomas Bonald, Alexandre Proutiere
Reconciling "priors" & "priors" without prejudice? Remi Gribonval, Pierre Machart
Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis Nikhil Rao, Christopher Cox, Rob Nowak, Timothy T. Rogers
Sensor Selection in High-Dimensional Gaussian Trees with Nuisances Daniel S. Levine, Jonathan P. How
Sequential Transfer in Multi-armed Bandit with Finite Set of Models Mohammad Gheshlaghi azar, Alessandro Lazaric, Emma Brunskill
Buy-in-Bulk Active Learning Liu Yang, Jaime Carbonell
Contrastive Learning Using Spectral Methods James Y. Zou, Daniel J. Hsu, David C. Parkes, Ryan P. Adams
Message Passing Inference with Chemical Reaction Networks Nils E. Napp, Ryan P. Adams
Eluder Dimension and the Sample Complexity of Optimistic Exploration Dan Russo, Benjamin Van Roy
Learning word embeddings efficiently with noise-contrastive estimation Andriy Mnih, Koray Kavukcuoglu
Sparse Inverse Covariance Estimation with Calibration Tuo Zhao, Han Liu
Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization Julien Mairal
Sinkhorn Distances: Lightspeed Computation of Optimal Transport Marco Cuturi
Speedup Matrix Completion with Side Information: Application to Multi-Label Learning Miao Xu, Rong Jin, Zhi-Hua Zhou
Compete to Compute Rupesh K. Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, Juergen Schmidhuber
Fast Determinantal Point Process Sampling with Application to Clustering Byungkon Kang
Information-theoretic lower bounds for distributed statistical estimation with communication constraints Yuchen Zhang, John Duchi, Michael I. Jordan, Martin J. Wainwright
Projected Natural Actor-Critic Philip S. Thomas, William C. Dabney, Stephen Giguere, Sridhar Mahadevan
How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal Jacob Abernethy, Peter L. Bartlett, Rafael Frongillo, Andre Wibisono
Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests Yacine Jernite, Yonatan Halpern, David Sontag
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion Franz Kiraly, Louis Theran
Learning the Local Statistics of Optical Flow Dan Rosenbaum, Daniel Zoran, Yair Weiss
Aggregating Optimistic Planning Trees for Solving Markov Decision Processes Gunnar Kedenburg, Raphael Fonteneau, Remi Munos
Robust learning of low-dimensional dynamics from large neural ensembles David Pfau, Eftychios A. Pnevmatikakis, Liam Paninski
Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising Min Xu, Tao Qin, Tie-Yan Liu
Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization Nataliya Shapovalova, Michalis Raptis, Leonid Sigal, Greg Mori
A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables Jing Xiang, Seyoung Kim
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited Matthias Hein, Simon Setzer, Leonardo Jost, Syama Sundar Rangapuram
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints Rishabh K. Iyer, Jeff A. Bilmes
Scalable Inference for Logistic-Normal Topic Models Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng, Bo Zhang
Spectral methods for neural characterization using generalized quadratic models Il Memming Park, Evan W. Archer, Nicholas Priebe, Jonathan W. Pillow
Universal models for binary spike patterns using centered Dirichlet processes Il Memming Park, Evan W. Archer, Kenneth Latimer, Jonathan W. Pillow
Synthesizing Robust Plans under Incomplete Domain Models Tuan A. Nguyen, Subbarao Kambhampati, Minh Do
Integrated Non-Factorized Variational Inference Shaobo Han, Xuejun Liao, Lawrence Carin
Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions Ari Pakman, Liam Paninski
Symbolic Opportunistic Policy Iteration for Factored-Action MDPs Aswin Raghavan, Roni Khardon, Alan Fern, Prasad Tadepalli
Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions Yasin Abbasi, Peter L. Bartlett, Varun Kanade, Yevgeny Seldin, Csaba Szepesvari
Flexible sampling of discrete data correlations without the marginal distributions Alfredo Kalaitzis, Ricardo Silva
One-shot learning by inverting a compositional causal process Brenden M. Lake, Ruslan R. Salakhutdinov, Josh Tenenbaum
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. Michel Besserve, Nikos K. Logothetis, Prof. Bernhard Schölkopf
Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis James R. Voss, Luis Rademacher, Mikhail Belkin
Deep Neural Networks for Object Detection Christian Szegedy, Alexander Toshev, Dumitru Erhan
Geometric optimisation on positive definite matrices for elliptically contoured distributions Suvrit Sra, Reshad Hosseini
Sign Cauchy Projections and Chi-Square Kernel Ping Li, Gennady Samorodnitsk, John Hopcroft
Relevance Topic Model for Unstructured Social Group Activity Recognition Fang Zhao, Yongzhen Huang, Liang Wang, Tieniu Tan
k-Prototype Learning for 3D Rigid Structures Hu Ding, Ronald Berezney, Jinhui Xu
Restricting exchangeable nonparametric distributions Sinead A. Williamson, Steve N. MacEachern, Eric P. Xing
Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting Shunan Zhang, Angela J. Yu
Probabilistic Movement Primitives Alexandros Paraschos, Christian Daniel, Jan R. Peters, Gerhard Neumann
Policy Shaping: Integrating Human Feedback with Reinforcement Learning Shane Griffith, Kaushik Subramanian, Jonathan Scholz, Charles Isbell, Andrea L. Thomaz
Multilinear Dynamical Systems for Tensor Time Series Mark Rogers, Lei Li, Stuart J. Russell
Deep content-based music recommendation Aaron van den Oord, Sander Dieleman, Benjamin Schrauwen
A Stability-based Validation Procedure for Differentially Private Machine Learning Kamalika Chaudhuri, Staal A. Vinterbo
Capacity of strong attractor patterns to model behavioural and cognitive prototypes Abbas Edalat
Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA Vincent Q. Vu, Juhee Cho, Jing Lei, Karl Rohe
Cluster Trees on Manifolds Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry Wasserman
Bayesian inference for low rank spatiotemporal neural receptive fields Mijung Park, Jonathan W. Pillow
Adaptive Submodular Maximization in Bandit Setting Victor Gabillon, Branislav Kveton, Zheng Wen, Brian Eriksson, S. Muthukrishnan
Generalized Method-of-Moments for Rank Aggregation Hossein Azari Soufiani, William Chen, David C. Parkes, Lirong Xia
Analyzing Hogwild Parallel Gaussian Gibbs Sampling Matthew Johnson, James Saunderson, Alan Willsky
Minimax Optimal Algorithms for Unconstrained Linear Optimization Brendan McMahan, Jacob Abernethy
(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings Abhradeep Guha Thakurta, Adam Smith
Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes
Σ-Optimality for Active Learning on Gaussian Random Fields Yifei Ma, Roman Garnett, Jeff Schneider
Learning Kernels Using Local Rademacher Complexity Corinna Cortes, Marius Kloft, Mehryar Mohri
Annealing between distributions by averaging moments Roger B. Grosse, Chris J. Maddison, Ruslan R. Salakhutdinov
Optimizing Instructional Policies Robert V. Lindsey, Michael C. Mozer, William J. Huggins, Harold Pashler
Translating Embeddings for Modeling Multi-relational Data Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko
Phase Retrieval using Alternating Minimization Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi
Real-Time Inference for a Gamma Process Model of Neural Spiking David E. Carlson, Vinayak Rao, Joshua T. Vogelstein, Lawrence Carin
Understanding Dropout Pierre Baldi, Peter J. Sadowski
The Power of Asymmetry in Binary Hashing Behnam Neyshabur, Nati Srebro, Ruslan R. Salakhutdinov, Yury Makarychev, Payman Yadollahpour
Estimation, Optimization, and Parallelism when Data is Sparse John Duchi, Michael I. Jordan, Brendan McMahan
A multi-agent control framework for co-adaptation in brain-computer interfaces Josh S. Merel, Roy Fox, Tony Jebara, Liam Paninski
Modeling Overlapping Communities with Node Popularities Prem K. Gopalan, Chong Wang, David Blei
Learning from Limited Demonstrations Beomjoon Kim, Amir massoud Farahmand, Joelle Pineau, Doina Precup
On the Complexity and Approximation of Binary Evidence in Lifted Inference Guy Van den Broeck, Adnan Darwiche
On the Representational Efficiency of Restricted Boltzmann Machines James Martens, Arkadev Chattopadhya, Toni Pitassi, Richard Zemel
Memory Limited, Streaming PCA Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain
An Approximate, Efficient LP Solver for LP Rounding Srikrishna Sridhar, Stephen Wright, Christopher Re, Ji Liu, Victor Bittorf, Ce Zhang
Linear decision rule as aspiration for simple decision heuristics Özgür Şimşek
On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation Harikrishna Narasimhan, Shivani Agarwal
Bayesian inference as iterated random functions with applications to sequential inference in graphical models Arash Amini, XuanLong Nguyen
Compressive Feature Learning Hristo S. Paskov, Robert West, John C. Mitchell, Trevor Hastie
Moment-based Uniform Deviation Bounds for k-means and Friends Matus J. Telgarsky, Sanjoy Dasgupta
Fast Template Evaluation with Vector Quantization Mohammad Amin Sadeghi, David Forsyth
Context-sensitive active sensing in humans Sheeraz Ahmad, He Huang, Angela J. Yu
A New Convex Relaxation for Tensor Completion Bernardino Romera-Paredes, Massimiliano Pontil
Variational Planning for Graph-based MDPs Qiang Cheng, Qiang Liu, Feng Chen, Alexander T. Ihler
Convex Two-Layer Modeling Özlem Aslan, Hao Cheng, Xinhua Zhang, Dale Schuurmans
Sketching Structured Matrices for Faster Nonlinear Regression Haim Avron, Vikas Sindhwani, David Woodruff
(More) Efficient Reinforcement Learning via Posterior Sampling Ian Osband, Dan Russo, Benjamin Van Roy
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition Adel Javanmard, Andrea Montanari
Efficient Exploration and Value Function Generalization in Deterministic Systems Zheng Wen, Benjamin Van Roy
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces Mahdi Milani Fard, Yuri Grinberg, Amir massoud Farahmand, Joelle Pineau, Doina Precup
Learning and using language via recursive pragmatic reasoning about other agents Nathaniel J. Smith, Noah Goodman, Michael Frank
Learning Stochastic Inverses Andreas Stuhlmüller, Jacob Taylor, Noah Goodman
Learning invariant representations and applications to face verification Qianli Liao, Joel Z. Leibo, Tomaso Poggio
Optimization, Learning, and Games with Predictable Sequences Sasha Rakhlin, Karthik Sridharan
Adaptivity to Local Smoothness and Dimension in Kernel Regression Samory Kpotufe, Vikas Garg
Adaptive dropout for training deep neural networks Jimmy Ba, Brendan Frey
Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral StreamDaniel L. Yamins, Ha Hong, Charles Cadieu, James J. DiCarlo
Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex Sam Patterson, Yee Whye Teh
Distributed Representations of Words and Phrases and their Compositionality Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, Jeff Dean
Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel Tai Qin, Karl Rohe
Analyzing the Harmonic Structure in Graph-Based Learning Xiao-Ming Wu, Zhenguo Li, Shih-Fu Chang
Recurrent linear models of simultaneously-recorded neural populations Marius Pachitariu, Biljana Petreska, Maneesh Sahani
Scalable Influence Estimation in Continuous-Time Diffusion Networks Nan Du, Le Song, Manuel Gomez Rodriguez, Hongyuan Zha
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl Edward Rasmussen
BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep K. Ravikumar, Russell Poldrack
The Fast Convergence of Incremental PCA Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund
Multisensory Encoding, Decoding, and Identification Aurel A. Lazar, Yevgeniy Slutskiy
Adaptive Anonymity via b-Matching Krzysztof M. Choromanski, Tony Jebara, Kui Tang
Optimal integration of visual speed across different spatiotemporal frequency channels Matjaz Jogan, Alan A. Stocker
Matrix factorization with binary components Martin Slawski, Matthias Hein, Pavlo Lutsik
Learning to Pass Expectation Propagation Messages Nicolas Heess, Daniel Tarlow, John Winn
Robust Low Rank Kernel Embeddings of Multivariate Distributions Le Song, Bo Dai

Advances in Neural Information Processing Systems 27 (NIPS 2014)

The papers below appear in Advances in Neural Information Processing Systems 27 edited by Z. Ghahramani and M. Welling and C. Cortes and N.D. Lawrence and K.Q. Weinberger .
They are proceedings from the conference, "Neural Information Processing Systems 2014."

Kernel Mean Estimation via Spectral Filtering Krikamol Muandet, Bharath Sriperumbudur, Prof. Bernhard Schölkopf
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models Yichuan Zhang, Charles Sutton
Communication Efficient Distributed Machine Learning with the Parameter Server Mu Li, David G. Andersen, Alexander J. Smola, Kai Yu
The Infinite Mixture of Infinite Gaussian Mixtures Halid Z. Yerebakan, Bartek Rajwa, Murat Dundar
Robust Classification Under Sample Selection Bias Anqi Liu, Brian Ziebart
Zeta Hull Pursuits: Learning Nonconvex Data Hulls Yuanjun Xiong, Wei Liu, Deli Zhao, Xiaoou Tang
Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction Katerina Fragkiadaki, Marta Salas, Pablo Arbelaez, Jitendra Malik
Sparse Space-Time Deconvolution for Calcium Image Analysis Ferran Diego Andilla, Fred A. Hamprecht
Restricted Boltzmann machines modeling human choice Takayuki Osogami, Makoto Otsuka
Multiscale Fields of Patterns Pedro Felzenszwalb, John G. Oberlin
large scale canonical correlation analysis with iterative least squares Yichao Lu, Dean P. Foster
Altitude Training: Strong Bounds for Single-Layer Dropout Stefan Wager, William Fithian, Sida Wang, Percy S. Liang
Rounding-based Moves for Metric Labeling M. Pawan Kumar
Parallel Double Greedy Submodular Maximization Xinghao Pan, Stefanie Jegelka, Joseph E. Gonzalez, Joseph K. Bradley, Michael I. Jordan
Multivariate Regression with Calibration Han Liu, Lie Wang, Tuo Zhao
Exact Post Model Selection Inference for Marginal Screening Jason D. Lee, Jonathan E. Taylor
On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification Yingzhen Yang, Feng Liang, Shuicheng Yan, Zhangyang Wang, Thomas S. Huang
Just-In-Time Learning for Fast and Flexible Inference S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John Winn
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation Ohad Shamir
Quantized Kernel Learning for Feature Matching Danfeng Qin, Xuanli Chen, Matthieu Guillaumin, Luc V. Gool
Parallel Direction Method of Multipliers Huahua Wang, Arindam Banerjee, Zhi-Quan Luo
(Almost) No Label No Cry Giorgio Patrini, Richard Nock, Paul Rivera, Tiberio Caetano
Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards Omar Besbes, Yonatan Gur, Assaf Zeevi
Object Localization based on Structural SVM using Privileged Information Jan Feyereisl, Suha Kwak, Jeany Son, Bohyung Han
Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations Zhenyao Zhu, Ping Luo, Xiaogang Wang, Xiaoou Tang
Shape and Illumination from Shading using the Generic Viewpoint Assumption Daniel Zoran, Dilip Krishnan, Jose Bento, Bill Freeman
Parallel Sampling of HDPs using Sub-Cluster Splits Jason Chang, John W. Fisher III
From MAP to Marginals: Variational Inference in Bayesian Submodular Models Josip Djolonga, Andreas Krause
Robust Logistic Regression and Classification Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan
Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities Tianbao Yang, Rong Jin
A Unified Semantic Embedding: Relating Taxonomies and Attributes Sung Ju Hwang, Leonid Sigal
Transportability from Multiple Environments with Limited Experiments: Completeness Results Elias Bareinboim, Judea Pearl
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner
Causal Inference through a Witness Protection Program Ricardo Silva, Robin Evans
Incremental Clustering: The Case for Extra Clusters Margareta Ackerman, Sanjoy Dasgupta
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Low Rank Approximation Lower Bounds in Row-Update Streams David Woodruff
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An Autoencoder Approach to Learning Bilingual Word Representations Sarath Chandar A P, Stanislas Lauly, Hugo Larochelle, Mitesh Khapra, Balaraman Ravindran, Vikas C. Raykar, Amrita Saha
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Deep Fragment Embeddings for Bidirectional Image Sentence Mapping Andrej Karpathy, Armand Joulin, Fei Fei F. Li
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Modeling Deep Temporal Dependencies with Recurrent Grammar Cells"" Vincent Michalski, Roland Memisevic, Kishore Konda
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Local Linear Convergence of Forward--Backward under Partial Smoothness Jingwei Liang, Jalal Fadili, Gabriel Peyré
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A Probabilistic Framework for Multimodal Retrieval using Integrative Indian Buffet Process Bahadir Ozdemir, Larry S. Davis
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Structure Regularization for Structured Prediction Xu Sun
On Multiplicative Multitask Feature Learning Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun
Multivariate f-divergence Estimation With Confidence Kevin Moon, Alfred Hero
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Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets Adarsh Prasad, Stefanie Jegelka, Dhruv Batra
Do Deep Nets Really Need to be Deep? Jimmy Ba, Rich Caruana
Dynamic Rank Factor Model for Text Streams Shaobo Han, Lin Du, Esther Salazar, Lawrence Carin
Generative Adversarial Nets Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
Testing Unfaithful Gaussian Graphical Models De Wen Soh, Sekhar C. Tatikonda
Global Sensitivity Analysis for MAP Inference in Graphical Models Jasper De Bock, Cassio P. de Campos, Alessandro Antonucci
Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain Charles Y. Zheng, Franco Pestilli, Ariel Rokem
Efficient Minimax Signal Detection on Graphs Jing Qian, Venkatesh Saligrama
Cone-Constrained Principal Component Analysis Yash Deshpande, Andrea Montanari, Emile Richard
On Communication Cost of Distributed Statistical Estimation and Dimensionality Ankit Garg, Tengyu Ma, Huy Nguyen
Computing Nash Equilibria in Generalized Interdependent Security Games Hau Chan, Luis E. Ortiz
Consistent Binary Classification with Generalized Performance Metrics Oluwasanmi O. Koyejo, Nagarajan Natarajan, Pradeep K. Ravikumar, Inderjit S. Dhillon
Greedy Subspace Clustering Dohyung Park, Constantine Caramanis, Sujay Sanghavi
Deterministic Symmetric Positive Semidefinite Matrix Completion William E. Bishop, Byron M. Yu
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition Hanie Sedghi, Anima Anandkumar, Edmond Jonckheere
Online combinatorial optimization with stochastic decision sets and adversarial losses Gergely Neu, Michal Valko
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature Tom Gunter, Michael A. Osborne, Roman Garnett, Philipp Hennig, Stephen J. Roberts
Multi-Scale Spectral Decomposition of Massive Graphs Si Si, Donghyuk Shin, Inderjit S. Dhillon, Beresford N. Parlett
The limits of squared Euclidean distance regularization Michal Derezinski, Manfred K. Warmuth
Bregman Alternating Direction Method of Multipliers Huahua Wang, Arindam Banerjee
Multitask learning meets tensor factorization: task imputation via convex optimization Kishan Wimalawarne, Masashi Sugiyama, Ryota Tomioka
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A. Gibson, Eric P. Xing
Scalable Inference for Neuronal Connectivity from Calcium Imaging Alyson K. Fletcher, Sundeep Rangan
Structure learning of antiferromagnetic Ising models Guy Bresler, David Gamarnik, Devavrat Shah
The Noisy Power Method: A Meta Algorithm with Applications Moritz Hardt, Eric Price
Algorithm selection by rational metareasoning as a model of human strategy selection Falk Lieder, Dillon Plunkett, Jessica B. Hamrick, Stuart J. Russell, Nicholas Hay, Thomas Griffiths
Extremal Mechanisms for Local Differential Privacy Peter Kairouz, Sewoong Oh, Pramod Viswanath
Global Belief Recursive Neural Networks Romain Paulus, Richard Socher, Christopher D. Manning
A statistical model for tensor PCA Emile Richard, Andrea Montanari
Real-Time Decoding of an Integrate and Fire Encoder Shreya Saxena, Munther Dahleh
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning Brendan McMahan, Matthew Streeter
On the Number of Linear Regions of Deep Neural Networks Guido F. Montufar, Razvan Pascanu, Kyunghyun Cho, Yoshua Bengio
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization Yann N. Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, Yoshua Bengio
Extracting Latent Structure From Multiple Interacting Neural Populations Joao Semedo, Amin Zandvakili, Adam Kohn, Christian K. Machens, Byron M. Yu
Learning with Fredholm Kernels Qichao Que, Mikhail Belkin, Yusu Wang
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models Michalis Titsias RC AUEB, Christopher Yau
Optimizing Energy Production Using Policy Search and Predictive State Representations Yuri Grinberg, Doina Precup, Michel Gendreau
Scaling-up Importance Sampling for Markov Logic Networks Deepak Venugopal, Vibhav G. Gogate
Optimal Neural Codes for Control and Estimation Alex K. Susemihl, Ron Meir, Manfred Opper
Graph Clustering With Missing Data: Convex Algorithms and Analysis Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi
Scale Adaptive Blind Deblurring Haichao Zhang, Jianchao Yang
Weighted importance sampling for off-policy learning with linear function approximation A. Rupam Mahmood, Hado P. van Hasselt, Richard S. Sutton
Information-based learning by agents in unbounded state spaces Shariq A. Mobin, James A. Arnemann, Fritz Sommer
Exponential Concentration of a Density Functional Estimator Shashank Singh, Barnabas Poczos
Scalable Kernel Methods via Doubly Stochastic Gradients Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina F. Balcan, Le Song
Fast Training of Pose Detectors in the Fourier Domain João F. Henriques, Pedro Martins, Rui F. Caseiro, Jorge Batista
An Accelerated Proximal Coordinate Gradient Method Qihang Lin, Zhaosong Lu, Lin Xiao
Communication-Efficient Distributed Dual Coordinate Ascent Martin Jaggi, Virginia Smith, Martin Takac, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan
Simple MAP Inference via Low-Rank Relaxations Roy Frostig, Sida Wang, Percy S. Liang, Christopher D. Manning
A* Sampling Chris J. Maddison, Daniel Tarlow, Tom Minka
A Bayesian model for identifying hierarchically organised states in neural population activity Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke
Sequence to Sequence Learning with Neural Networks Ilya Sutskever, Oriol Vinyals, Quoc V. Le
Improved Distributed Principal Component Analysis Yingyu Liang, Maria-Florina F. Balcan, Vandana Kanchanapally, David Woodruff
Sparse Polynomial Learning and Graph Sketching Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G. Dimakis, Adam Klivans
Tight Continuous Relaxation of the Balanced k-Cut Problem Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein
Mondrian Forests: Efficient Online Random Forests Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Expectation-Maximization for Learning Determinantal Point Processes Jennifer A. Gillenwater, Alex Kulesza, Emily Fox, Ben Taskar
Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs David I. Inouye, Pradeep K. Ravikumar, Inderjit S. Dhillon
Streaming, Memory Limited Algorithms for Community Detection Se-Young Yun, marc lelarge, Alexandre Proutiere
Content-based recommendations with Poisson factorization Prem K. Gopalan, Laurent Charlin, David Blei
A Statistical Decision-Theoretic Framework for Social Choice Hossein Azari Soufiani, David C. Parkes, Lirong Xia
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices Jianbo Yang, Xuejun Liao, Minhua Chen, Lawrence Carin
Bayesian Sampling Using Stochastic Gradient Thermostats Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D. Skeel, Hartmut Neven
On Sparse Gaussian Chain Graph Models Calvin McCarter, Seyoung Kim
Orbit Regularization Renato Negrinho, Andre Martins
Efficient Minimax Strategies for Square Loss Games Wouter M. Koolen, Alan Malek, Peter L. Bartlett
A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs Miles Lopes
Large-Margin Convex Polytope Machine Alex Kantchelian, Michael C. Tschantz, Ling Huang, Peter L. Bartlett, Anthony D. Joseph, J. D. Tygar
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models Yarin Gal, Mark van der Wilk, Carl Edward Rasmussen
Learning Distributed Representations for Structured Output Prediction Vivek Srikumar, Christopher D. Manning
Convex Deep Learning via Normalized Kernels Özlem Aslan, Xinhua Zhang, Dale Schuurmans
Tight convex relaxations for sparse matrix factorization Emile Richard, Guillaume R. Obozinski, Jean-Philippe Vert
Learning to Search in Branch and Bound Algorithms He He, Hal Daume III, Jason M. Eisner
An Integer Polynomial Programming Based Framework for Lifted MAP Inference Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav G. Gogate
Conditional Random Field Autoencoders for Unsupervised Structured Prediction Waleed Ammar, Chris Dyer, Noah A. Smith
How transferable are features in deep neural networks? Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson
Accelerated Mini-batch Randomized Block Coordinate Descent Method Tuo Zhao, Mo Yu, Yiming Wang, Raman Arora, Han Liu
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard L. Lewis, Xiaoshi Wang
A Latent Source Model for Online Collaborative Filtering Guy Bresler, George H. Chen, Devavrat Shah
Distributed Bayesian Posterior Sampling via Moment Sharing Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang
Learning with Pseudo-Ensembles Philip Bachman, Ouais Alsharif, Doina Precup
Learning Time-Varying Coverage Functions Nan Du, Yingyu Liang, Maria-Florina F. Balcan, Le Song
Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time Zhaoran Wang, Huanran Lu, Han Liu
Discriminative Metric Learning by Neighborhood Gerrymandering Shubhendu Trivedi, David Mcallester, Greg Shakhnarovich
Finding a sparse vector in a subspace: Linear sparsity using alternating directions Qing Qu, Ju Sun, John Wright
Asynchronous Anytime Sequential Monte Carlo Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh
Discrete Graph Hashing Wei Liu, Cun Mu, Sanjiv Kumar, Shih-Fu Chang
Feedback Detection for Live Predictors Stefan Wager, Nick Chamandy, Omkar Muralidharan, Amir Najmi
Rates of Convergence for Nearest Neighbor Classification Kamalika Chaudhuri, Sanjoy Dasgupta
Consistency of weighted majority votes Daniel Berend, Aryeh Kontorovich
Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling Mingyuan Zhou
Zero-shot recognition with unreliable attributes Dinesh Jayaraman, Kristen Grauman
Concavity of reweighted Kikuchi approximation Po-Ling Loh, Andre Wibisono
Online Decision-Making in General Combinatorial Spaces Arun Rajkumar, Shivani Agarwal
Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning Mohammad Taha Bahadori, Qi (Rose) Yu, Yan Liu
Clustered factor analysis of multineuronal spike data Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski
Algorithms for CVaR Optimization in MDPs Yinlam Chow, Mohammad Ghavamzadeh
Factoring Variations in Natural Images with Deep Gaussian Mixture Models Aaron van den Oord, Benjamin Schrauwen
Partition-wise Linear Models Hidekazu Oiwa, Ryohei Fujimaki
LSDA: Large Scale Detection through Adaptation Judy Hoffman, Sergio Guadarrama, Eric S. Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, Kate Saenko
Deep Networks with Internal Selective Attention through Feedback Connections Marijn F. Stollenga, Jonathan Masci, Faustino Gomez, Juergen Schmidhuber
Parallel Feature Selection Inspired by Group Testing Yingbo Zhou, Utkarsh Porwal, Ce Zhang, Hung Q. Ngo, XuanLong Nguyen, Christopher Ré, Venu Govindaraju
Low-Rank Time-Frequency Synthesis Cédric Févotte, Matthieu Kowalski
Pre-training of Recurrent Neural Networks via Linear Autoencoders Luca Pasa, Alessandro Sperduti
Semi-supervised Learning with Deep Generative Models Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation Mingjun Zhong, Nigel Goddard, Charles Sutton
Stochastic variational inference for hidden Markov models Nicholas Foti, Jason Xu, Dillon Laird, Emily Fox
A Wild Bootstrap for Degenerate Kernel Tests Kacper P. Chwialkowski, Dino Sejdinovic, Arthur Gretton
Biclustering Using Message Passing Luke O'Connor, Soheil Feizi
Fast Kernel Learning for Multidimensional Pattern Extrapolation Andrew Wilson, Elad Gilboa, John P. Cunningham, Arye Nehorai
Learning on graphs using Orthonormal Representation is Statistically Consistent Rakesh Shivanna, Chiranjib Bhattacharyya
Spectral k-Support Norm Regularization Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos
Unsupervised learning of an efficient short-term memory network Pietro Vertechi, Wieland Brendel, Christian K. Machens
Quantized Estimation of Gaussian Sequence Models in Euclidean Balls Yuancheng Zhu, John Lafferty
Learning a Concept Hierarchy from Multi-labeled Documents Viet-An Nguyen, Jordan L. Boyd-Graber, Philip Resnik, Jonathan Chang
Variational Gaussian Process State-Space Models Roger Frigola, Yutian Chen, Carl Edward Rasmussen
Fast Prediction for Large-Scale Kernel Machines Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon

Advances in Neural Information Processing Systems 28 (NIPS 2015)

The papers below appear in Advances in Neural Information Processing Systems 28 edited by C. Cortes and N.D. Lawrence and D.D. Lee and M. Sugiyama and R. Garnett .
They are proceedings from the conference, "Neural Information Processing Systems 2015."

Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing Nihar Bhadresh Shah, Denny Zhou
Learning with Symmetric Label Noise: The Importance of Being Unhinged Brendan van Rooyen, Aditya Menon, Robert C. Williamson
Algorithmic Stability and Uniform Generalization Ibrahim M. Alabdulmohsin
Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models Theodoros Tsiligkaridis, Theodoros Tsiligkaridis, Keith Forsythe
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling Xiaocheng Shang, Zhanxing Zhu, Benedict Leimkuhler, Amos J. Storkey
Robust Portfolio Optimization Huitong Qiu, Fang Han, Han Liu, Brian Caffo
Logarithmic Time Online Multiclass prediction Anna E. Choromanska, John Langford
Planar Ultrametrics for Image Segmentation Julian E. Yarkony, Charless Fowlkes
Expressing an Image Stream with a Sequence of Natural Sentences Cesc C. Park, Gunhee Kim
Parallel Correlation Clustering on Big Graphs Xinghao Pan, Dimitris Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun
Space-Time Local Embeddings Ke Sun, Jun Wang, Alexandros Kalousis, Stephane Marchand-Maillet
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear MeasurementsQinqing Zheng, John Lafferty
Smooth Interactive Submodular Set Cover Bryan D. He, Yisong Yue
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning Jiajun Wu, Ilker Yildirim, Joseph J. Lim, Bill Freeman, Josh Tenenbaum
On the Pseudo-Dimension of Nearly Optimal Auctions Jamie H. Morgenstern, Tim Roughgarden
Unlocking neural population non-stationarities using hierarchical dynamics models Mijung Park, Gergo Bohner, Jakob H. Macke
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltan Szabo, Lars Buesing, Maneesh Sahani
Color Constancy by Learning to Predict Chromaticity from Luminance Ayan Chakrabarti
Fast and Accurate Inference of Plackett–Luce Models Lucas Maystre, Matthias Grossglauser
Probabilistic Line Searches for Stochastic Optimization Maren Mahsereci, Philipp Hennig
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets Armand Joulin, Tomas Mikolov
Where are they looking? Adria Recasens, Aditya Khosla, Carl Vondrick, Antonio Torralba
The Pareto Regret Frontier for Bandits Tor Lattimore
On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors Andrea Montanari, Daniel Reichman, Ofer Zeitouni
Measuring Sample Quality with Stein's Method Jackson Gorham, Lester Mackey
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution Yan Huang, Wei Wang, Liang Wang
Bounding errors of Expectation-Propagation Guillaume P. Dehaene, Simon Barthelmé
A fast, universal algorithm to learn parametric nonlinear embeddings Miguel A. Carreira-Perpinan, Max Vladymyrov
Texture Synthesis Using Convolutional Neural Networks Leon Gatys, Alexander S. Ecker, Matthias Bethge
Extending Gossip Algorithms to Distributed Estimation of U-statistics Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon
Streaming, Distributed Variational Inference for Bayesian Nonparametrics Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How
Learning visual biases from human imagination Carl Vondrick, Hamed Pirsiavash, Aude Oliva, Antonio Torralba
Smooth and Strong: MAP Inference with Linear Convergence Ofer Meshi, Mehrdad Mahdavi, Alex Schwing
Copeland Dueling Bandits Masrour Zoghi, Zohar S. Karnin, Shimon Whiteson, Maarten de Rijke
Optimal Ridge Detection using Coverage Risk Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry Wasserman
Top-k Multiclass SVM Maksim Lapin, Matthias Hein, Bernt Schiele
Policy Evaluation Using the Ω-Return Philip S. Thomas, Scott Niekum, Georgios Theocharous, George Konidaris
Orthogonal NMF through Subspace Exploration Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros G. Dimakis
Stochastic Online Greedy Learning with Semi-bandit Feedbacks Tian Lin, Jian Li, Wei Chen
Deeply Learning the Messages in Message Passing Inference Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring David Kappel, Stefan Habenschuss, Robert Legenstein, Wolfgang Maass
Accelerated Proximal Gradient Methods for Nonconvex Programming Huan Li, Zhouchen Lin
Approximating Sparse PCA from Incomplete Data ABHISEK KUNDU, Petros Drineas, Malik Magdon-Ismail
Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, james m. robins
Column Selection via Adaptive Sampling Saurabh Paul, Malik Magdon-Ismail, Petros Drineas
HONOR: Hybrid Optimization for NOn-convex Regularized problems Pinghua Gong, Jieping Ye
3D Object Proposals for Accurate Object Class Detection Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G. Berneshawi, Huimin Ma, Sanja Fidler, Raquel Urtasun
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits Huasen Wu, R. Srikant, Xin Liu, Chong Jiang
Tensorizing Neural Networks Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, Dmitry P. Vetrov
Parallelizing MCMC with Random Partition Trees Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson
A Reduced-Dimension fMRI Shared Response Model Po-Hsuan (Cameron) Chen, Janice Chen, Yaara Yeshurun, Uri Hasson, James Haxby, Peter J. Ramadge
Spectral Learning of Large Structured HMMs for Comparative Epigenomics Chicheng Zhang, Jimin Song, Kamalika Chaudhuri, Kevin Chen
Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability Xia Qu, Prashant Doshi
Estimating Mixture Models via Mixtures of Polynomials Sida Wang, Arun Tejasvi Chaganty, Percy S. Liang
On the Global Linear Convergence of Frank-Wolfe Optimization Variants Simon Lacoste-Julien, Martin Jaggi
Deep Knowledge Tracing Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, Jascha Sohl-Dickstein
Rethinking LDA: Moment Matching for Discrete ICA Anastasia Podosinnikova, Francis Bach, Simon Lacoste-Julien
Efficient Compressive Phase Retrieval with Constrained Sensing Vectors Sohail Bahmani, Justin Romberg
Barrier Frank-Wolfe for Marginal Inference Rahul G. Krishnan, Simon Lacoste-Julien, David Sontag
Learning Theory and Algorithms for Forecasting Non-stationary Time Series Vitaly Kuznetsov, Mehryar Mohri
Compressive spectral embedding: sidestepping the SVD Dinesh Ramasamy, Upamanyu Madhow
A Nonconvex Optimization Framework for Low Rank Matrix Estimation Tuo Zhao, Zhaoran Wang, Han Liu
Automatic Variational Inference in Stan Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman,