bde: A Python Package for Bayesian Deep Ensembles via MILE
Authors: Vyron Arvanitis, Angelos Aslanidis, Emanuel Sommer et al.
Summary
arXiv:2605. 14146v1 Announce Type: new Abstract: bde is a user-friendly Python package for Bayesian Deep Ensembles with a particular focus on tabular data.
Relevance
Read next because bde: A Python Package for Bayesian Deep Ensembles via MILE overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: class, implement, chain. Source: arxiv cs.LG (Machine Learning).
Abstract
arXiv:2605.14146v1 Announce Type: new 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.