2021
JMLR
JMLR 2021
Nonparametric Modeling of Higher-Order Interactions via Hypergraphons
Abstract
We study statistical and algorithmic aspects of using hypergraphons, that are limits of large hypergraphs, for modeling higher-order interactions. Although hypergraphons are extremely powerful from a modeling perspective, we consider a restricted class of Simple Lipschitz Hypergraphons (SLH), that are amenable to practically efficient estimation. We also provide rates of convergence for our estimator that are optimal for the class of SLH. Simulation results are provided to corroborate the theory. [abs] [ pdf ][ bib ] © JMLR 2021. (edit, beta)
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Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— lipschitz condition
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Hot Topic Early Bird
— convergence rate
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— Artificial Intelligence, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Reinforcement Learning, Security & Privacy