2014
NIPS
NeurIPS 2014
Distributed Parameter Estimation in Probabilistic Graphical Models
Abstract
This paper presents foundational theoretical results on distributed parameter estimation for undirected probabilistic graphical models. It introduces a general condition on composite likelihood decompositions of these models which guarantees the global consistency of distributed estimators, provided the local estimators are consistent.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Trend Setter
— Federated Learning
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Keyword Pioneer
— distributed parameter estimation
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Hot Topic Early Bird
— probabilistic modeling
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Artificial Intelligence > Learning Paradigms > Federated Learning
Machine Learning > Optimization & Theory > Distributed Learning
Machine Learning > Optimization & Theory > Statistical Learning
Machine Learning > Bayesian & Probabilistic > Graphical Models
Machine Learning > Learning Types > Distributed Learning