Scalable Posterior Uncertainty for Flexible Density-Based Clustering
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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.
arXiv:2604. 23083v2 Announce Type: replace-cross Abstract: Generative approaches to clustering provide information on geometric properties of clusters, whereas discriminative approaches provide boundaries between clusters.
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.
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.
arXiv:2606. 00327v1 Announce Type: cross Abstract: Clustering is widely used across the sciences as the foundation for downstream data-driven scientific discoveries.
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.