[2603.27955] Symbolic Density Estimation: A Decompositional Approach

Computer Science > Machine Learning arXiv:2603.27955 (cs) [Submitted on 30 Mar 2026] Title:Symbolic Density Estimation: A Decompositional Approach Authors:Angelo Rajendram, Xieting Chu, Vijay Ganesh, Max Fieg, Aishik Ghosh View a PDF of the paper titled Symbolic Density Estimation: A Decompositional Approach, by Angelo Rajendram and 4 other authors View PDF HTML (experimental) Abstract:We introduce AI-Kolmogorov, a novel framework for Symbolic Density Estimation (SymDE). Symbolic regression (SR) has been effectively used to produce interpretable models in standard regression settings but its applicability to density estimation tasks has largely been unexplored. To address the SymDE task we introduce a multi-stage pipeline: (i) problem decomposition through clustering and/or probabilistic graphical model structure learning; (ii) nonparametric density estimation; (iii) support estimation; and finally (iv) SR on the density estimate. We demonstrate the efficacy of AI-Kolmogorov on synthetic mixture models, multivariate normal distributions, and three exotic distributions, two of which are motivated by applications in high-energy physics. We show that AI-Kolmogorov can discover underlying distributions or otherwise provide valuable insight into the mathematical expressions describing them. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603.27955 [cs.LG]   (or arXiv:2603.27955v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.27955 Focus to learn more arXiv-...