Graphical models for machine learning and digital communication
Graphical models for machine learning and digital communication
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
The ζ (2) limit in the random assignment problem
Random Structures & Algorithms
Stereo Matching Using Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Finding Deformable Shapes Using Loopy Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Table extraction using conditional random fields
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Tree consistency and bounds on the performance of the max-product algorithm and its generalizations
Statistics and Computing
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A new look at survey propagation and its generalizations
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Survey propagation: An algorithm for satisfiability
Random Structures & Algorithms
Non-convex optimization and rate control for multi-class services in the Internet
IEEE/ACM Transactions on Networking (TON)
Predicting protein functions with message passing algorithms
Bioinformatics
Random Structures & Algorithms
Walk-Sums and Belief Propagation in Gaussian Graphical Models
The Journal of Machine Learning Research
Distributed rate allocation for inelastic flows
IEEE/ACM Transactions on Networking (TON)
Convergence of min-sum message passing for quadratic optimization
IEEE Transactions on Information Theory
Loopy belief propagation and Gibbs measures
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
The capacity of low-density parity-check codes under message-passing decoding
IEEE Transactions on Information Theory
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
An analysis of belief propagation on the turbo decoding graph with Gaussian densities
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Fundamental design issues for the future Internet
IEEE Journal on Selected Areas in Communications
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We propose a message-passing paradigm for resource allocation problems. This serves to connect ideas from the message-passing literature, which has primarily grown out of the communications, statistical physics, and artificial intelligence communities, with a problem central to operations research. This also provides a new framework for decentralized management that generalizes price-based systems by allowing incentives to vary across activities and consumption levels. We demonstrate that message-based incentives, which are characterized by a new equilibrium concept, lead to system-optimal behavior for convex resource allocation problems yet yield allocations superior to those from price-based incentives for nonconvex problems. We describe a distributed and asynchronous message-passing algorithm for computing equilibrium messages and allocations, and we demonstrate its merits in the context of a network resource allocation problem.