Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference

Jonathan Lee, Aldo Pacchiano, Michael Jordan
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:3003-3014, 2020.

Abstract

Maximum a posteriori (MAP) inference is a fundamental computational paradigm for statistical inference. In the setting of graphical models, MAP inference entails solving a combinatorial optimization problem to find the most likely configuration of the discrete-valued model. Linear programming (LP) relaxations in the Sherali-Adams hierarchy are widely used to attempt to solve this problem, and smooth message passing algorithms have been proposed to solve regularized versions of these LPs with great success. This paper leverages recent work in entropy-regularized LPs to analyze convergence rates of a class of edge-based smooth message passing algorithms to epsilon-optimality in the relaxation. With an appropriately chosen regularization constant, we present a theoretical guarantee on the number of iterations sufficient to recover the true integral MAP solution when the LP is tight and the solution is unique.

Cite this Paper

BibTeX
@InProceedings{pmlr-v108-lee20b, title = {Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference}, author = {Lee, Jonathan and Pacchiano, Aldo and Jordan, Michael}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {3003--3014}, year = {2020}, editor = {Chiappa, Silvia and Calandra, Roberto}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/lee20b/lee20b.pdf}, url = {https://proceedings.mlr.press/v108/lee20b.html}, abstract = {Maximum a posteriori (MAP) inference is a fundamental computational paradigm for statistical inference. In the setting of graphical models, MAP inference entails solving a combinatorial optimization problem to find the most likely configuration of the discrete-valued model. Linear programming (LP) relaxations in the Sherali-Adams hierarchy are widely used to attempt to solve this problem, and smooth message passing algorithms have been proposed to solve regularized versions of these LPs with great success. This paper leverages recent work in entropy-regularized LPs to analyze convergence rates of a class of edge-based smooth message passing algorithms to epsilon-optimality in the relaxation. With an appropriately chosen regularization constant, we present a theoretical guarantee on the number of iterations sufficient to recover the true integral MAP solution when the LP is tight and the solution is unique.} }
Endnote
%0 Conference Paper %T Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference %A Jonathan Lee %A Aldo Pacchiano %A Michael Jordan %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-lee20b %I PMLR %P 3003--3014 %U https://proceedings.mlr.press/v108/lee20b.html %V 108 %X Maximum a posteriori (MAP) inference is a fundamental computational paradigm for statistical inference. In the setting of graphical models, MAP inference entails solving a combinatorial optimization problem to find the most likely configuration of the discrete-valued model. Linear programming (LP) relaxations in the Sherali-Adams hierarchy are widely used to attempt to solve this problem, and smooth message passing algorithms have been proposed to solve regularized versions of these LPs with great success. This paper leverages recent work in entropy-regularized LPs to analyze convergence rates of a class of edge-based smooth message passing algorithms to epsilon-optimality in the relaxation. With an appropriately chosen regularization constant, we present a theoretical guarantee on the number of iterations sufficient to recover the true integral MAP solution when the LP is tight and the solution is unique.
APA
Lee, J., Pacchiano, A. & Jordan, M.. (2020). Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:3003-3014 Available from https://proceedings.mlr.press/v108/lee20b.html.

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