[2602.24201] Flow-Based Density Ratio Estimation for Intractable Distributions with Applications in Genomics

Computer Science > Machine Learning arXiv:2602.24201 (cs) [Submitted on 27 Feb 2026] Title:Flow-Based Density Ratio Estimation for Intractable Distributions with Applications in Genomics Authors:Egor Antipov, Alessandro Palma, Lorenzo Consoli, Stephan Günnemann, Andrea Dittadi, Fabian J. Theis View a PDF of the paper titled Flow-Based Density Ratio Estimation for Intractable Distributions with Applications in Genomics, by Egor Antipov and 5 other authors View PDF HTML (experimental) 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 and covariates. While exact-likelihood models such as normalizing flows offer a promising approach to density ratio estimation, naive flow-based evaluations are computationally expensive, as they require simulating costly likelihood integrals for each distribution separately. In this work, we leverage condition-aware flow matching to derive a single dynamical formulation for tracking density ratios along generative trajectories. We demonstrate competitive performance on simulated benchmarks for closed-form ratio estimation, and show that our method supports versatile tasks in single-cell genomics data analysis, where likelihood-based comparisons of cellular states across experimental conditions enable treatment effect estimation and batch correction evaluat...