arXiv Machine Learning June 2, 2026 By Egor Antipov, Alessandro Palma, Lorenzo Consoli, Stephan G\"unnemann, Andrea Dittadi, Fabian J. Theis

Flow-Based Density Ratio Estimation for Intractable Distributions with Applications in Genomics

Read the original on arXiv Machine Learning →

arXiv:2602. 24201v2 Announce Type: replace Abstract: Estimating density ratios between pairs of intractable data distributions is a core problem in probabilistic modeling, enabling principled comparisons of sample likelihoods under different data-generating processes across conditions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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