NeurIPS 2006

204 papers

A Bayesian Approach to Diffusion Models of Decision-Making and Response Time Michael D. Lee, Ian G. Fuss, Daniel J. Navarro

A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation Yee W. Teh, David Newman, Max Welling

A Complexity-Distortion Approach to Joint Pattern Alignment Andrea Vedaldi, Stefano Soatto

A Humanlike Predictor of Facial Attractiveness Amit Kagian, Gideon Dror, Tommer Leyvand, Daniel Cohen-or, Eytan Ruppin

A Kernel Method for the Two-Sample-Problem Arthur Gretton, Karsten Borgwardt, Malte Rasch, Bernhard Schölkopf, Alex J. Smola

A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida

A Local Learning Approach for Clustering Mingrui Wu, Bernhard Schölkopf

A Nonparametric Approach to Bottom-up Visual Saliency Wolf Kienzle, Felix A. Wichmann, Matthias O. Franz, Bernhard Schölkopf

A Nonparametric Bayesian Method for Inferring Features from Similarity Judgments Daniel J. Navarro, Thomas L. Griffiths

A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems David Barber, Bertrand Mesot

A PAC-Bayes Risk Bound for General Loss Functions Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand

A Probabilistic Algorithm Integrating Source Localization and Noise Suppression of MEG and EEG Data Johanna M. Zumer, Hagai T. Attias, Kensuke Sekihara, Srikantan S. Nagarajan

A Recipe for Optimizing a Time-Histogram Hideaki Shimazaki, Shigeru Shinomoto

A Scalable Machine Learning Approach to Go Lin Wu, Pierre F. Baldi

A Selective Attention Multi--Chip System with Dynamic Synapses and Spiking Neurons Chiara Bartolozzi, Giacomo Indiveri

A Small World Threshold for Economic Network Formation Eyal Even-dar, Michael Kearns

A Switched Gaussian Process for Estimating Disparity and Segmentation in Binocular Stereo Oliver Williams

A Theory of Retinal Population Coding Eizaburo Doi, Michael S. Lewicki

Accelerated Variational Dirichlet Process Mixtures Kenichi Kurihara, Max Welling, Nikos Vlassis

Active Learning for Misspecified Generalized Linear Models Francis R. Bach

AdaBoost Is Consistent Peter L. Bartlett, Mikhail Traskin

Adaptive Spatial Filters with Predefined Region of Interest for EEG Based Brain-Computer-Interfaces Moritz Grosse-wentrup, Klaus Gramann, Martin Buss

Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models Mark Johnson, Thomas L. Griffiths, Sharon Goldwater

Aggregating Classification Accuracy Across Time: Application to Single Trial EEG Steven Lemm, Christin Schäfer, Gabriel Curio

An Application of Reinforcement Learning to Aerobatic Helicopter Flight Pieter Abbeel, Adam Coates, Morgan Quigley, Andrew Y. Ng

An Approach to Bounded Rationality Eli Ben-sasson, Ehud Kalai, Adam Kalai

An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models S. S. Keerthi, Vikas Sindhwani, Olivier Chapelle

An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments Michael I. Mandel, Daniel P. Ellis, Tony Jebara

An Information Theoretic Framework for Eukaryotic Gradient Sensing Joseph M. Kimmel, Richard M. Salter, Peter J. Thomas

An Oracle Inequality for Clipped Regularized Risk Minimizers Ingo Steinwart, Don Hush, Clint Scovel

Analysis of Contour Motions Ce Liu, William T. Freeman, Edward H. Adelson

Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization Rey Ramírez, Jason Palmer, Scott Makeig, Bhaskar D. Rao, David P. Wipf

Analysis of Representations for Domain Adaptation Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira

Approximate Correspondences in High Dimensions Kristen Grauman, Trevor Darrell

Approximate Inference Using Planar Graph Decomposition Amir Globerson, Tommi S. Jaakkola

Attentional Processing on a Spike-Based VLSI Neural Network Yingxue Wang, Rodney J. Douglas, Shih-Chii Liu

Attribute-Efficient Learning of Decision Lists and Linear Threshold Functions Under Unconcentrated Distributions Philip M. Long, Rocco Servedio

Automated Hierarchy Discovery for Planning in Partially Observable Environments Laurent Charlin, Pascal Poupart, Romy Shioda

Balanced Graph Matching Timothee Cour, Praveen Srinivasan, Jianbo Shi

Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure Jennifer Listgarten, Radford M. Neal, Sam T. Roweis, Rachel Puckrin, Sean Cutler

Bayesian Ensemble Learning Hugh A. Chipman, Edward I. George, Robert E. Mcculloch

Bayesian Image Super-Resolution, Continued Lyndsey C. Pickup, David P. Capel, Stephen J. Roberts, Andrew Zisserman

Bayesian Model Scoring in Markov Random Fields Sridevi Parise, Max Welling

Bayesian Policy Gradient Algorithms Mohammad Ghavamzadeh, Yaakov Engel

Blind Motion Deblurring Using Image Statistics Anat Levin

Blind Source Separation for Over-Determined Delayed Mixtures Lars Omlor, Martin Giese

Boosting Structured Prediction for Imitation Learning J. A. Bagnell, Joel Chestnutt, David M. Bradley, Nathan D. Ratliff

Branch and Bound for Semi-Supervised Support Vector Machines Olivier Chapelle, Vikas Sindhwani, S. S. Keerthi

Causal Inference in Sensorimotor Integration Konrad P. Körding, Joshua B. Tenenbaum

Chained Boosting Christian R. Shelton, Wesley Huie, Kin F. Kan

Clustering Appearance and Shape by Learning Jigsaws Anitha Kannan, John Winn, Carsten Rother

Clustering Under Prior Knowledge with Application to Image Segmentation Dong S. Cheng, Vittorio Murino, Mário Figueiredo

Combining Causal and Similarity-Based Reasoning Charles Kemp, Patrick Shafto, Allison Berke, Joshua B. Tenenbaum

Comparative Gene Prediction Using Conditional Random Fields Jade P. Vinson, David Decaprio, Matthew D. Pearson, Stacey Luoma, James E. Galagan

Computation of Similarity Measures for Sequential Data Using Generalized Suffix Trees Konrad Rieck, Pavel Laskov, Sören Sonnenburg

Conditional Mean Field Peter Carbonetto, Nando D. Freitas

Conditional Random Sampling: A Sketch-Based Sampling Technique for Sparse Data Ping Li, Kenneth W. Church, Trevor J. Hastie

Context Dependent Amplification of Both Rate and Event-Correlation in a VLSI Network of Spiking Neurons Elisabetta Chicca, Giacomo Indiveri, Rodney J. Douglas

Context Effects in Category Learning: An Investigation of Four Probabilistic Models Michael Mozer, Michael Shettel, Michael P. Holmes

Convergence of Laplacian Eigenmaps Mikhail Belkin, Partha Niyogi

Convex Repeated Games and Fenchel Duality Shai Shalev-shwartz, Yoram Singer

Correcting Sample Selection Bias by Unlabeled Data Jiayuan Huang, Arthur Gretton, Karsten Borgwardt, Bernhard Schölkopf, Alex J. Smola

Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods Matthias Seeger

Data Integration for Classification Problems Employing Gaussian Process Priors Mark Girolami, Mingjun Zhong

Denoising and Dimension Reduction in Feature Space Mikio L. Braun, Klaus-Robert Müller, Joachim M. Buhmann

Detecting Humans via Their Pose Alessandro Bissacco, Ming-Hsuan Yang, Stefano Soatto

Differential Entropic Clustering of Multivariate Gaussians Jason V. Davis, Inderjit S. Dhillon

Dirichlet-Enhanced Spam Filtering Based on Biased Samples Steffen Bickel, Tobias Scheffer

Distributed Inference in Dynamical Systems Stanislav Funiak, Carlos Guestrin, Rahul Sukthankar, Mark A. Paskin

Doubly Stochastic Normalization for Spectral Clustering Ron Zass, Amnon Shashua

Dynamic Foreground/Background Extraction from Images and Videos Using Random Patches Le Lu, Gregory D. Hager

Effects of Stress and Genotype on Meta-Parameter Dynamics in Reinforcement Learning Gediminas Lukšys, Jérémie Knüsel, Denis Sheynikhovich, Carmen Sandi, Wulfram Gerstner

Efficient Learning of Sparse Representations with an Energy-Based Model Marc'aurelio Ranzato, Christopher Poultney, Sumit Chopra, Yann L. Cun

Efficient Methods for Privacy Preserving Face Detection Shai Avidan, Moshe Butman

Efficient Sparse Coding Algorithms Honglak Lee, Alexis Battle, Rajat Raina, Andrew Y. Ng

Efficient Structure Learning of Markov Networks Using $l_1$-Regularization Su-in Lee, Varun Ganapathi, Daphne Koller

Emergence of Conjunctive Visual Features by Quadratic Independent Component Analysis J.t. Lindgren, Aapo Hyvärinen

Fast Computation of Graph Kernels Karsten Borgwardt, Nicol N. Schraudolph, S.v.n. Vishwanathan

Fast Discriminative Visual Codebooks Using Randomized Clustering Forests Frank Moosmann, Bill Triggs, Frederic Jurie

Fast Iterative Kernel PCA Nicol N. Schraudolph, Simon Günter, S.v.n. Vishwanathan

Fundamental Limitations of Spectral Clustering Boaz Nadler, Meirav Galun

Game Theoretic Algorithms for Protein-DNA Binding Luis Pérez-breva, Luis E. Ortiz, Chen-hsiang Yeang, Tommi S. Jaakkola

Gaussian and Wishart Hyperkernels Risi Kondor, Tony Jebara

Generalized Maximum Margin Clustering and Unsupervised Kernel Learning Hamed Valizadegan, Rong Jin

Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space Wenye Li, Kin-hong Lee, Kwong-sak Leung

Geometric Entropy Minimization (GEM) for Anomaly Detection and Localization Alfred O. Hero

Graph Laplacian Regularization for Large-Scale Semidefinite Programming Kilian Q. Weinberger, Fei Sha, Qihui Zhu, Lawrence K. Saul

Graph-Based Visual Saliency Jonathan Harel, Christof Koch, Pietro Perona

Greedy Layer-Wise Training of Deep Networks Yoshua Bengio, Pascal Lamblin, Dan Popovici, Hugo Larochelle

Handling Advertisements of Unknown Quality in Search Advertising Sandeep Pandey, Christopher Olston

Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space Kyung-ah Sohn, Eric P. Xing

Hierarchical Dirichlet Processes with Random Effects Seyoung Kim, Padhraic Smyth

High-Dimensional Graphical Model Selection Using $\ell_1$-Regularized Logistic Regression Martin J. Wainwright, John D. Lafferty, Pradeep K. Ravikumar

Hyperparameter Learning for Graph Based Semi-Supervised Learning Algorithms Xinhua Zhang, Wee S. Lee

iLSTD: Eligibility Traces and Convergence Analysis Alborz Geramifard, Michael Bowling, Martin Zinkevich, Richard S. Sutton

Image Retrieval and Classification Using Local Distance Functions Andrea Frome, Yoram Singer, Jitendra Malik

Implicit Online Learning with Kernels Li Cheng, Dale Schuurmans, Shaojun Wang, Terry Caelli, S.v.n. Vishwanathan

Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions Christian Walder, Olivier Chapelle, Bernhard Schölkopf

In-Network PCA and Anomaly Detection Ling Huang, Xuanlong Nguyen, Minos Garofalakis, Michael I. Jordan, Anthony Joseph, Nina Taft

Inducing Metric Violations in Human Similarity Judgements Julian Laub, Klaus-Robert Müller, Felix A. Wichmann, Jakob H. Macke

Inferring Network Structure from Co-Occurrences Michael G. Rabbat, Mário Figueiredo, Robert Nowak

Information Bottleneck for Non Co-Occurrence Data Yevgeny Seldin, Noam Slonim, Naftali Tishby

Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons Stefan Klampfl, Wolfgang Maass, Robert A. Legenstein

Isotonic Conditional Random Fields and Local Sentiment Flow Yi Mao, Guy Lebanon

Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm Robert Jenssen, Torbjørn Eltoft, Mark Girolami, Deniz Erdogmus

Kernels on Structured Objects Through Nested Histograms Marco Cuturi, Kenji Fukumizu

Large Margin Component Analysis Lorenzo Torresani, Kuang-chih Lee

Large Margin Hidden Markov Models for Automatic Speech Recognition Fei Sha, Lawrence K. Saul

Large Margin Multi-Channel Analog-to-Digital Conversion with Applications to Neural Prosthesis Amit Gore, Shantanu Chakrabartty

Large Scale Hidden Semi-Markov SVMs Gunnar Rätsch, Sören Sonnenburg

Large-Scale Sparsified Manifold Regularization Ivor W. Tsang, James T. Kwok

Learnability and the Doubling Dimension Yi Li, Philip M. Long

Learning Annotated Hierarchies from Relational Data Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, Joshua B. Tenenbaum

Learning Dense 3D Correspondence Florian Steinke, Volker Blanz, Bernhard Schölkopf

Learning from Multiple Sources Koby Crammer, Michael Kearns, Jennifer Wortman

Learning Motion Style Synthesis from Perceptual Observations Lorenzo Torresani, Peggy Hackney, Christoph Bregler

Learning Nonparametric Models for Probabilistic Imitation David B. Grimes, Daniel R. Rashid, Rajesh P. Rao

Learning on Graph with Laplacian Regularization Rie K. Ando, Tong Zhang

Learning Structural Equation Models for fMRI Enrico Simonotto, Heather Whalley, Stephen Lawrie, Lawrence Murray, David Mcgonigle, Amos J. Storkey

Learning Time-Intensity Profiles of Human Activity Using Non-Parametric Bayesian Models Alexander T. Ihler, Padhraic Smyth

Learning to Be Bayesian Without Supervision Martin Raphan, Eero P. Simoncelli

Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields Chi-hoon Lee, Shaojun Wang, Feng Jiao, Dale Schuurmans, Russell Greiner

Learning to Parse Images of Articulated Bodies Deva Ramanan

Learning to Rank with Nonsmooth Cost Functions Christopher J. Burges, Robert Ragno, Quoc V. Le

Learning to Traverse Image Manifolds Piotr Dollár, Vincent Rabaud, Serge J. Belongie

Learning with Hypergraphs: Clustering, Classification, and Embedding Dengyong Zhou, Jiayuan Huang, Bernhard Schölkopf

Linearly-Solvable Markov Decision Problems Emanuel Todorov

Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning Peter Auer, Ronald Ortner

Logistic Regression for Single Trial EEG Classification Ryota Tomioka, Kazuyuki Aihara, Klaus-Robert Müller

Manifold Denoising Matthias Hein, Markus Maier

mAP-Reduce for Machine Learning on Multicore Cheng-tao Chu, Sang K. Kim, Yi-an Lin, Yuanyuan Yu, Gary Bradski, Kunle Olukotun, Andrew Y. Ng

Max-Margin Classification of Incomplete Data Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, Daphne Koller

Mixture Regression for Covariate Shift Masashi Sugiyama, Amos J. Storkey

MLLE: Modified Locally Linear Embedding Using Multiple Weights Zhenyue Zhang, Jing Wang

Modeling Dyadic Data with Binary Latent Factors Edward Meeds, Zoubin Ghahramani, Radford M. Neal, Sam T. Roweis

Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers

Modeling Human Motion Using Binary Latent Variables Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis

Modelling Transcriptional Regulation Using Gaussian Processes Neil D. Lawrence, Guido Sanguinetti, Magnus Rattray

Multi-Dynamic Bayesian Networks Karim Filali, Jeff A. Bilmes

Multi-Instance Multi-Label Learning with Application to Scene Classification Zhi-Li Zhang, Min-ling Zhang

Multi-Robot Negotiation: Approximating the Set of Subgame Perfect Equilibria in General-Sum Stochastic Games Chris Murray, Geoffrey J. Gordon

Multi-Task Feature Learning Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil

Multiple Instance Learning for Computer Aided Diagnosis Murat Dundar, Balaji Krishnapuram, R. B. Rao, Glenn M. Fung

Multiple Timescales and Uncertainty in Motor Adaptation Konrad P. Körding, Joshua B. Tenenbaum, Reza Shadmehr

Mutagenetic Tree Fisher Kernel Improves Prediction of HIV Drug Resistance from Viral Genotype Tobias Sing, Niko Beerenwinkel

Natural Actor-Critic for Road Traffic Optimisation Silvia Richter, Douglas Aberdeen, Jin Yu

Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints Carla P. Gomes, Ashish Sabharwal, Bart Selman

Neurophysiological Evidence of Cooperative Mechanisms for Stereo Computation Jason M. Samonds, Brian R. Potetz, Tai S. Lee

No-Regret Algorithms for Online Convex Programs Geoffrey J. Gordon

Non-Rigid Point Set Registration: Coherent Point Drift Andriy Myronenko, Xubo Song, Miguel Á. Carreira-Perpiñán

Nonlinear Physically-Based Models for Decoding Motor-Cortical Population Activity Gregory Shakhnarovich, Sung-phil Kim, Michael J. Black

Nonnegative Sparse PCA Ron Zass, Amnon Shashua

On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts Hariharan Narayanan, Mikhail Belkin, Partha Niyogi

On Transductive Regression Corinna Cortes, Mehryar Mohri

Online Classification for Complex Problems Using Simultaneous Projections Yonatan Amit, Shai Shalev-shwartz, Yoram Singer

Online Clustering of Moving Hyperplanes René Vidal

Optimal Change-Detection and Spiking Neurons Angela J. Yu

Optimal Single-Class Classification Strategies Ran El-Yaniv, Mordechai Nisenson

Ordinal Regression by Extended Binary Classification Ling Li, Hsuan-tien Lin

PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier Alexandre Lacasse, François Laviolette, Mario Marchand, Pascal Germain, Nicolas Usunier

Parameter Expanded Variational Bayesian Methods Tommi S. Jaakkola, Yuan Qi

Part-Based Probabilistic Point Matching Using Equivalence Constraints Graham Mcneill, Sethu Vijayakumar

Particle Filtering for Nonparametric Bayesian Matrix Factorization Frank Wood, Thomas L. Griffiths

PG-Means: Learning the Number of Clusters in Data Yu Feng, Greg Hamerly

Predicting Spike Times from Subthreshold Dynamics of a Neuron Ryota Kobayashi, Shigeru Shinomoto

Prediction on a Graph with a Perceptron Mark Herbster, Massimiliano Pontil

Randomized PCA Algorithms with Regret Bounds That Are Logarithmic in the Dimension Manfred K. Warmuth, Dima Kuzmin

Real-Time Adaptive Information-Theoretic Optimization of Neurophysiology Experiments Jeremy Lewi, Robert Butera, Liam Paninski

Recursive Attribute Factoring David Cohn, Deepak Verma, Karl Pfleger

Recursive ICA Honghao Shan, Lingyun Zhang, Garrison W. Cottrell

Reducing Calibration Time for Brain-Computer Interfaces: A Clustering Approach Matthias Krauledat, Michael Schröder, Benjamin Blankertz, Klaus-Robert Müller

Relational Learning with Gaussian Processes Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S. S. Keerthi

Robotic Grasping of Novel Objects Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Andrew Y. Ng

Sample Complexity of Policy Search with Known Dynamics Peter L. Bartlett, Ambuj Tewari

Scalable Discriminative Learning for Natural Language Parsing and Translation Joseph Turian, Benjamin Wellington, I. D. Melamed

Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds Benjamin I. Rubinstein, Peter L. Bartlett, J. H. Rubinstein

Similarity by Composition Oren Boiman, Michal Irani

Simplifying Mixture Models Through Function Approximation Kai Zhang, James T. Kwok

Single Channel Speech Separation Using Factorial Dynamics John R. Hershey, Trausti Kristjansson, Steven Rennie, Peder A. Olsen

Sparse Kernel Orthonormalized PLS for Feature Extraction in Large Data Sets Jerónimo Arenas-garcía, Kaare B. Petersen, Lars K. Hansen

Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation Gavin C. Cawley, Nicola L. Talbot, Mark Girolami

Sparse Representation for Signal Classification Ke Huang, Selin Aviyente

Speakers Optimize Information Density Through Syntactic Reduction T. F. Jaeger, Roger P. Levy

Stability of $k$-Means Clustering Alexander Rakhlin, Andrea Caponnetto

Statistical Modeling of Images with Fields of Gaussian Scale Mixtures Siwei Lyu, Eero P. Simoncelli

Stochastic Relational Models for Discriminative Link Prediction Kai Yu, Wei Chu, Shipeng Yu, Volker Tresp, Zhao Xu

Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds Gloria Haro, Gregory Randall, Guillermo Sapiro

Subordinate Class Recognition Using Relational Object Models Aharon B. Hillel, Daphna Weinshall

Support Vector Machines on a Budget Ofer Dekel, Yoram Singer

Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks Alexis Battle, Gal Chechik, Daphne Koller

Temporal Coding Using the Response Properties of Spiking Neurons Thomas Voegtlin

Temporal Dynamics of Information Content Carried by Neurons in the Primary Visual Cortex Danko Nikolić, Stefan Haeusler, Wolf Singer, Wolfgang Maass

The Neurodynamics of Belief Propagation on Binary Markov Random Fields Thomas Ott, Ruedi Stoop

The Robustness-Performance Tradeoff in Markov Decision Processes Huan Xu, Shie Mannor

Theory and Dynamics of Perceptual Bistability Paul R. Schrater, Rashmi Sundareswara

Tighter PAC-Bayes Bounds Amiran Ambroladze, Emilio Parrado-hernández, John S. Shawe-taylor

Towards a General Independent Subspace Analysis Fabian J. Theis

Training Conditional Random Fields for Maximum Labelwise Accuracy Samuel S. Gross, Olga Russakovsky, Chuong B. Do, Serafim Batzoglou

TrueSkill™: A Bayesian Skill Rating System Ralf Herbrich, Tom Minka, Thore Graepel

Uncertainty, Phase and Oscillatory Hippocampal Recall Máté Lengyel, Peter Dayan

Unified Inference for Variational Bayesian Linear Gaussian State-Space Models David Barber, Silvia Chiappa

Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing Yuanhao Chen, Long Zhu, Alan L. Yuille

Unsupervised Regression with Applications to Nonlinear System Identification Ali Rahimi, Ben Recht

Using Combinatorial Optimization Within Max-Product Belief Propagation Daniel Tarlow, Gal Elidan, Daphne Koller, John C. Duchi