Matrix analysis
On the cycle polytope of a binary matroid
Journal of Combinatorial Theory Series B
Theory of linear and integer programming
Theory of linear and integer programming
Abstract dynamic programming models under commutativity conditions
SIAM Journal on Control and Optimization
Integer and combinatorial optimization
Integer and combinatorial optimization
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The Boolean quadric polytope: some characteristics, facets and relatives
Mathematical Programming: Series A and B
Introduction to algorithms
Decomposition and optimization over cycles in binary matroids
Journal of Combinatorial Theory Series B
A hierarchy of relaxation between the continuous and convex hull representations
SIAM Journal on Discrete Mathematics
Elements of information theory
Elements of information theory
Fundamentals of speech recognition
Fundamentals of speech recognition
SIAM Review
Applied numerical linear algebra
Applied numerical linear algebra
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Determinant Maximization with Linear Matrix Inequality Constraints
SIAM Journal on Matrix Analysis and Applications
Efficient learning in Boltzmann machines using linear response theory
Neural Computation
Improving the mean field approximation via the use of mixture distributions
Learning in graphical models
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Foundations of statistical natural language processing
Foundations of statistical natural language processing
An Introduction to Variational Methods for Graphical Models
Machine Learning
Tractable variational structures for approximating graphical models
Proceedings of the 1998 conference on Advances in neural information processing systems II
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Learning Markov networks: maximum bounded tree-width graphs
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Approximation algorithms
Dynamic Programming and Stochastic Control
Dynamic Programming and Stochastic Control
Introduction to Linear Optimization
Introduction to Linear Optimization
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
Nonserial Dynamic Programming
Global Optimization with Polynomials and the Problem of Moments
SIAM Journal on Optimization
An Explicit Equivalent Positive Semidefinite Program for Nonlinear 0-1 Programs
SIAM Journal on Optimization
Multilocus linkage analysis by blocked Gibbs sampling
Statistics and Computing
Combining phylogenetic and hidden Markov models in biosequence analysis
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Variational Approximations between Mean Field Theory and the Junction Tree Algorithm
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Discrete Applied Mathematics
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Constraint Processing
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Stochastic processes on graphs with cycles: geometric and variational approaches
Stochastic processes on graphs with cycles: geometric and variational approaches
The Journal of Machine Learning Research
A Comparison of the Sherali-Adams, Lovász-Schrijver, and Lasserre Relaxations for 0--1 Programming
Mathematics of Operations Research
Tree consistency and bounds on the performance of the max-product algorithm and its generalizations
Statistics and Computing
Convex Optimization
On the uniqueness of loopy belief propagation fixed points
Neural Computation
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
A Linear Programming Formulation and Approximation Algorithms for the Metric Labeling Problem
SIAM Journal on Discrete Mathematics
Loopy Belief Propagation: Convergence and Effects of Message Errors
The Journal of Machine Learning Research
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Counting without sampling: new algorithms for enumeration problems using statistical physics
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Estimation and Marginalization Using the Kikuchi Approximation Methods
Neural Computation
Gaussian Processes for Classification: Mean-Field Algorithms
Neural Computation
Correctness of Local Probability Propagation in Graphical Models with Loops
Neural Computation
Quadratic programming relaxations for metric labeling and Markov random field MAP estimation
ICML '06 Proceedings of the 23rd international conference on Machine learning
Solving Markov Random Fields using Second Order Cone Programming Relaxations
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expectation Consistent Approximate Inference
The Journal of Machine Learning Research
Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting
The Journal of Machine Learning Research
Linear Programming Relaxations and Belief Propagation -- An Empirical Study
The Journal of Machine Learning Research
Walk-Sums and Belief Propagation in Gaussian Graphical Models
The Journal of Machine Learning Research
A new look at survey propagation and its generalizations
Journal of the ACM (JACM)
A Linear Programming Approach to Max-Sum Problem: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Truncating the Loop Series Expansion for Belief Propagation
The Journal of Machine Learning Research
Proceedings of the 25th international conference on Machine learning
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
First-Order Methods for Sparse Covariance Selection
SIAM Journal on Matrix Analysis and Applications
Information, Physics, and Computation
Information, Physics, and Computation
Convexity arguments for efficient minimization of the Bethe and Kikuchi free energies
Journal of Artificial Intelligence Research
Variational probabilistic inference and the QMR-DT network
Journal of Artificial Intelligence Research
Minimizing and learning energy functions for side-chain prediction
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Modern Coding Theory
Loopy belief propagation and Gibbs measures
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Approximate inference and constrained optimization
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Embedded trees: estimation of Gaussian Processes on graphs with cycles
IEEE Transactions on Signal Processing
Log-determinant relaxation for approximate inference in discrete Markov random fields
IEEE Transactions on Signal Processing - Part I
Convergence Analysis of Reweighted Sum-Product Algorithms
IEEE Transactions on Signal Processing
The generalized distributive law
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Improved low-density parity-check codes using irregular graphs
IEEE Transactions on Information Theory
The capacity of low-density parity-check codes under message-passing decoding
IEEE Transactions on Information Theory
Signal-space characterization of iterative decoding
IEEE Transactions on Information Theory
Tree-based reparameterization framework for analysis of sum-product and related algorithms
IEEE Transactions on Information Theory
Using linear programming to Decode Binary linear codes
IEEE Transactions on Information Theory
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
A new class of upper bounds on the log partition function
IEEE Transactions on Information Theory
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
LP Decoding Corrects a Constant Fraction of Errors
IEEE Transactions on Information Theory
Probabilistic Analysis of Linear Programming Decoding
IEEE Transactions on Information Theory
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
Iterative decoding of compound codes by probability propagation in graphical models
IEEE Journal on Selected Areas in Communications
Note: The expressive power of binary submodular functions
Discrete Applied Mathematics
Message family propagation for ising mean field based on iteration tree
Proceedings of the 18th ACM conference on Information and knowledge management
A variational inference framework for soft-in soft-out detection in multiple-access channels
IEEE Transactions on Information Theory
Message passing for maximum weight independent set
IEEE Transactions on Information Theory
A Survey of Statistical Network Models
Foundations and Trends® in Machine Learning
A Hierarchical and Contextual Model for Aerial Image Parsing
International Journal of Computer Vision
How do the structure and the parameters of Gaussian tree models affect structure learning?
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Optimization of structured mean field objectives
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Mean field variational approximation for continuous-time Bayesian networks
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
MAP estimation, message passing, and perfect graphs
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Convexifying the Bethe free energy
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Understanding cardinality estimation using entropy maximization
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A framework for mining interesting pattern sets
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
Learning Gaussian tree models: analysis of error exponents and extremal structures
IEEE Transactions on Signal Processing
Nonparametric belief propagation
Communications of the ACM
Bayesian Browsing Model: Exact Inference of Document Relevance from Petabyte-Scale Data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Variational inference for adaptor grammars
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing
The Journal of Machine Learning Research
Bayesian online learning of the hazard rate in change-point problems
Neural Computation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Entropy and margin maximization for structured output learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
MRF inference by k-fan decomposition and tight Lagrangian relaxation
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Mean Field Variational Approximation for Continuous-Time Bayesian Networks
The Journal of Machine Learning Research
Covariance in Unsupervised Learning of Probabilistic Grammars
The Journal of Machine Learning Research
SIAM Journal on Discrete Mathematics
A distributed CSMA algorithm for throughput and utility maximization in wireless networks
IEEE/ACM Transactions on Networking (TON)
Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data
International Journal of Computer Vision
A framework for mining interesting pattern sets
ACM SIGKDD Explorations Newsletter
Towards the geometry of estimation of distribution algorithms based on the exponential family
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Topology discovery of sparse random graphs with few participants
Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Hybrid tractability of valued constraint problems
Artificial Intelligence
Topology discovery of sparse random graphs with few participants
ACM SIGMETRICS Performance Evaluation Review - Performance evaluation review
Globally optimal image partitioning by multicuts
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Evaluation of a first-order primal-dual algorithm for MRF energy minimization
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
An alternating direction method for dual MAP LP relaxation
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Maximum entropy models and subjective interestingness: an application to tiles in binary databases
Data Mining and Knowledge Discovery
Accelerated training of max-margin Markov networks with kernels
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Trends and advances in speech recognition
IBM Journal of Research and Development
Towards a top-down and bottom-up bidirectional approach to joint information extraction
Proceedings of the 20th ACM international conference on Information and knowledge management
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
SIAM Journal on Imaging Sciences
Stochastic global optimization as a filtering problem
Journal of Computational Physics
The complexity of conservative valued CSPs
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Probabilistic management of OCR data using an RDBMS
Proceedings of the VLDB Endowment
Modeling the co-evolution of behaviors and social relationships using mobile phone data
Proceedings of the 10th International Conference on Mobile and Ubiquitous Multimedia
Understanding cardinality estimation using entropy maximization
ACM Transactions on Database Systems (TODS)
Communications of the ACM
Unsupervised semantic role induction with graph partitioning
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Algorithms for probabilistic latent tensor factorization
Signal Processing
Recursive sum-product algorithm for generalized outer-planar graphs
Information Processing Letters
Weakly convex coupling continuous cuts and shape priors
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Foundations and Trends® in Machine Learning
Structured Learning and Prediction in Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Mr. LDA: a flexible large scale topic modeling package using variational inference in MapReduce
Proceedings of the 21st international conference on World Wide Web
Recognition of dependent objects based on acyclic Markov models
Pattern Recognition and Image Analysis
A tractable combinatorial market maker using constraint generation
Proceedings of the 13th ACM Conference on Electronic Commerce
Efficient rank aggregation using partial data
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Learning 3D geological structure from drill-rig sensors for automated mining
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
A geometric view of conjugate priors
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Robust Bayesian Clustering for Replicated Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Tractable triangles and cross-free convexity in discrete optimisation
Journal of Artificial Intelligence Research
Intent-aware temporal query modeling for keyword suggestion
Proceedings of the 5th Ph.D. workshop on Information and knowledge
A convex discrete-continuous approach for Markov random fields
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
The complexity of conservative valued CSPs
Journal of the ACM (JACM)
Distributed Kalman smoothing in static Bayesian networks
Automatica (Journal of IFAC)
Panorama: a semantic-aware application search framework
Proceedings of the 16th International Conference on Extending Database Technology
Towards high-throughput gibbs sampling at scale: a study across storage managers
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Learning dependency-based compositional semantics
Computational Linguistics
The complexity of finite-valued CSPs
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
A probabilistic graphical model approach to stochastic multiscale partial differential equations
Journal of Computational Physics
Journal of Computational Physics
Human vs machine: establishing a human baseline for multimodal location estimation
Proceedings of the 21st ACM international conference on Multimedia
Variational inference in nonconjugate models
The Journal of Machine Learning Research
Stochastic variational inference
The Journal of Machine Learning Research
Hidden factors and hidden topics: understanding rating dimensions with review text
Proceedings of the 7th ACM conference on Recommender systems
Gaussian message propagation in d-order neighborhood for gaussian graphical model
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Computer Vision and Image Understanding
Energy distribution view for monotonic dual decomposition
International Journal of Approximate Reasoning
Dimensionality reduction with generalized linear models
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Environmental Modelling & Software
Active learning for networked data based on non-progressive diffusion model
Proceedings of the 7th ACM international conference on Web search and data mining
Accelerated training of max-margin Markov networks with kernels
Theoretical Computer Science
Variational algorithms for marginal MAP
The Journal of Machine Learning Research
Decomposition and Approximation of Loopy Bayesian Networks
Fundamenta Informaticae
A comparative study of novel robust clustering algorithms
Intelligent Data Analysis
Hi-index | 0.17 |
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fields, including bioinformatics, communication theory, statistical physics, combinatorial optimization, signal and image processing, information retrieval and statistical machine learning. Many problems that arise in specific instances — including the key problems of computing marginals and modes of probability distributions — are best studied in the general setting. Working with exponential family representations, and exploiting the conjugate duality between the cumulant function and the entropy for exponential families, we develop general variational representations of the problems of computing likelihoods, marginal probabilities and most probable configurations. We describe how a wide variety of algorithms — among them sum-product, cluster variational methods, expectation-propagation, mean field methods, max-product and linear programming relaxation, as well as conic programming relaxations — can all be understood in terms of exact or approximate forms of these variational representations. The variational approach provides a complementary alternative to Markov chain Monte Carlo as a general source of approximation methods for inference in large-scale statistical models.