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Federico Matteucci

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#machine learning Preprint Oct 2026

Conditional Kernel Stein Discrepancy

Kernel Stein discrepancies (KSDs) provide a versatile tool for comparing distributions. One of their main applications is in quantifying the goodness-of-fit (GoF) between a data-generating distribution and a prescribed target distribution. In this work, we study the related problem of conditional GoF quantification: gi...

Federico Matteucci, Florian Kalinke · 0 citations

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