[2603.03188] Scalable Uncertainty Quantification for Black-Box Density-Based Clustering

Statistics > Machine Learning arXiv:2603.03188 (stat) [Submitted on 3 Mar 2026] Title:Scalable Uncertainty Quantification for Black-Box Density-Based Clustering Authors:Nicola Bariletto, Stephen G. Walker View a PDF of the paper titled Scalable Uncertainty Quantification for Black-Box Density-Based Clustering, by Nicola Bariletto and 1 other authors View PDF HTML (experimental) Abstract:We introduce a novel framework for uncertainty quantification in clustering. By combining the martingale posterior paradigm with density-based clustering, uncertainty in the estimated density is naturally propagated to the clustering structure. The approach scales effectively to high-dimensional and irregularly shaped data by leveraging modern neural density estimators and GPU-friendly parallel computation. We establish frequentist consistency guarantees and validate the methodology on synthetic and real data. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) MSC classes: 62C10 (Primary), 62H30, 68T37 (Secondary) Cite as: arXiv:2603.03188 [stat.ML]   (or arXiv:2603.03188v1 [stat.ML] for this version)   https://doi.org/10.48550/arXiv.2603.03188 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Nicola Bariletto [view email] [v1] Tue, 3 Mar 2026 17:46:49 UTC (762 KB) Full-text links: Access Paper: View a PDF of the paper titled Scalable Uncertainty Quantification for Black-Box Density-Based Clustering, by Nicola Bariletto and 1 other...