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024 7 _ |a 10.1088/2632-2153/ae0def
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100 1 _ |a Drnevich, Matthew
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245 _ _ |a Neural quasiprobabilistic likelihood ratio estimation with negatively weighted data
260 _ _ |a Bristol
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520 _ _ |a MLST-IOPscience-Header.pngPurpose-Led Publishing logo.Part of the Machine Learning Series logo.Paper • The following article is Open accessNeural quasiprobabilistic likelihood ratio estimation with negatively weighted dataMatthew Drnevich, Stephen Jiggins*, Judith Katzy and Kyle CranmerPublished 28 October 2025 • © 2025 The Author(s). Published by IOP Publishing LtdMachine Learning: Science and Technology, Volume 6, Number 4Focus on ML and the Physical SciencesCitation Matthew Drnevich et al 2025 Mach. Learn.: Sci. Technol. 6 045023DOI 10.1088/2632-2153/ae0defDownload Article PDFArticle metrics172 Total downloads11 citation on Dimensions.SubmitSubmit to this JournalShare this articleAbstractMotivated by real-world situations found in high energy particle physics, we consider a generalization of the likelihood-ratio estimation task to a quasiprobabilistic setting where probability densities can be negative, and to importance sampling where the importance weights can be negative. The presence of negative densities and negative weights, pose an array of challenges to traditional neural likelihood ratio (LR) estimation methods. We address these challenges by introducing a novel loss function. In addition, we introduce a new model architecture based on the decomposition of a LR using signed mixture models, providing a second strategy for overcoming these challenges. Finally, we demonstrate our approach on a pedagogical example and a real-world example from particle physics.
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700 1 _ |a Jiggins, Stephen
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