Abstract:bde is a user-friendly Python package for Bayesian Deep Ensembles with a particular focus on tabular data. Built on an efficient JAX implementation of the sampling-based inference method Microcanonical Langevin Ensembles (MILE), it provides scikit-learn compatible estimators for fast training, efficient Markov Chain Monte Carlo sampling, and uncertainty quantification in both regression and classification tasks.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.14146 [cs.LG] |
| (or arXiv:2605.14146v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.14146 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Emanuel Sommer [view email]
[v1]
Wed, 13 May 2026 21:52:49 UTC (6 KB)