bde: A Python Package for Bayesian Deep Ensembles via MILE
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866909041428529152 |
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| author | Arvanitis, Vyron Aslanidis, Angelos Sommer, Emanuel Rügamer, David |
| author_facet | Arvanitis, Vyron Aslanidis, Angelos Sommer, Emanuel Rügamer, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14146 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | bde: A Python Package for Bayesian Deep Ensembles via MILE Arvanitis, Vyron Aslanidis, Angelos Sommer, Emanuel Rügamer, David Machine Learning 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. |
| title | bde: A Python Package for Bayesian Deep Ensembles via MILE |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.14146 |