arXiv Machine Learning
Oct 12

Model-free Bayesian Clustering and Multimodal Posterior Summarisation

The paper introduces a model‑free, noise‑aware Bayesian clustering framework that builds a martingale posterior over clusterings without specifying a likelihood or prior, avoiding costly MCMC and variational methods. It handles irregular cluster shapes and allows parallel posterior sampling. To interpret the high‑dimensional posterior, a Wasserstein‑type summarization compresses many draws into a small set of weighted representatives using a novel noise‑aware loss, while marginalising uncertainty in the density level and providing theoretical guarantees.

By Qing Li, Sara Wade
arXiv Machine Learning
Sep 17

Bracketing Uncertainty in Clustering Under the Manifold Hypothesis

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 Machine Learning
Jun 19

Variational Consensus Monte Carlo for Bayesian Mixture

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 Machine Learning
Jun 2

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

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.

By Egor Antipov, Alessandro Palma, Lorenzo Consoli, Stephan G\"unnemann, Andrea Dittadi, Fabian J. Theis