arXiv:2603.03188v3 Announce Type: replace-cross
Abstract: Density-based clustering is a well-established framework that defines clusters through topological or geometric features of the data-generati...
By Nicola Bariletto, Stephen G. Walker
arXiv:2606. 19643v1 Announce Type: cross Abstract: Motivated by the privacy, sensitivity and sharing limitations of health data, we present a comprehensive pipeline for inference of Bayesian mixture models within a federated learning setting, i.
By Julie Fendler, Francesca L. Crowe, Tom Marshall, Sylvia Richardson, Paul D. W. Kirk
arXiv:2610.09153v1 Announce Type: cross
Abstract: Contemporary data in many scientific domains, such as images, media streams, biomedical omics, are naturally modeled as probability distributions in...
By Chenqin Xu, Yisha Yao
arXiv:2610.33177v1 Announce Type: new
Abstract: The Gaussian Mixture Model (abbrv. GMM) is a classic generative model for clustered data, for which spectral clustering based on dimension reduction vi...
By Xiaoyu Dong, Subhro Ghosh, Soumendu Sundar Mukherjee, Himasish Talukdar
The paper formalizes a geometric tradeoff between ambient separation and sampling gaps to determine when distinct manifold components can be reliably separated in clustering. It introduces a threshold phenomenon for mutual‑k‑nearest‑neighbor graphs, defining an uncertainty zone where the number of clusters cannot be identified. The authors propose Manifold‑Based Clustering (MBC), which outputs a bracket interval quantifying this uncertainty rather than forcing a single cluster count.
By Savik Kinger, Luciano Dyballa, Steven W. Zucker
arXiv:2609.36911v1 Announce Type: new
Abstract: In this thesis I develop methods for statistical inference when the distributions arising from complex biological systems are multi-modal, geometricall...
By Oskar Kviman