Approximated Structured Prediction for Learning Large Scale Graphical Models
Tamir Hazan, Raquel Urtasun
TL;DR
This paper provides proofs for a primal-dual message-passing algorithm designed to enable approximated structured prediction in large-scale graphical models, facilitating scalable inference.
Contribution
It introduces a novel primal-dual message-passing framework for efficient approximate inference in large graphical models.
Findings
Proofs validating the proposed algorithm's correctness
Theoretical guarantees on convergence
Enhanced scalability for large graphical models
Abstract
This manuscripts contains the proofs for "A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction".
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Taxonomy
TopicsMachine Learning and Data Classification · Machine Learning and Algorithms · Bayesian Modeling and Causal Inference