Universal Functional Regression with Neural Operator Flows Article Swipe

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Yaozhong Shi , Angela F. Gao , Zachary E. Ross , Kamyar Azizzadenesheli ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2404.02986

Regression on function spaces is typically limited to models with Gaussian process priors. We introduce the notion of universal functional regression, in which we aim to learn a prior distribution over non-Gaussian function spaces that remains mathematically tractable for functional regression. To do this, we develop Neural Operator Flows (OpFlow), an infinite-dimensional extension of normalizing flows. OpFlow is an invertible operator that maps the (potentially unknown) data function space into a Gaussian process, allowing for exact likelihood estimation of functional point evaluations. OpFlow enables robust and accurate uncertainty quantification via drawing posterior samples of the Gaussian process and subsequently mapping them into the data function space. We empirically study the performance of OpFlow on regression and generation tasks with data generated from Gaussian processes with known posterior forms and non-Gaussian processes, as well as real-world earthquake seismograms with an unknown closed-form distribution.

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Operator (biology) Regression Mathematics Computer science Statistics Biology Biochemistry Transcription factor Repressor Gene

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2404.02986
PDF: https://arxiv.org/pdf/2404.02986
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4394019821

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DOI: https://doi.org/10.48550/arxiv.2404.02986

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Title: Universal Functional Regression with Neural Operator Flows

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-04-03

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Authors: Yaozhong Shi, Angela F. Gao, Zachary E. Ross, Kamyar Azizzadenesheli

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Landing page: https://arxiv.org/abs/2404.02986

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PDF URL: https://arxiv.org/pdf/2404.02986

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Concepts: Operator (biology), Regression, Mathematics, Computer science, Statistics, Biology, Biochemistry, Transcription factor, Repressor, Gene

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