LGMay 13
bde: A Python Package for Bayesian Deep Ensembles via MILE
arXiv:2605.141463.31 citations
Predicted impact top 74% in LG · last 90 daysOriginality Synthesis-oriented
AI Analysis
Provides a practical tool for uncertainty quantification in deep learning for tabular data, but is an incremental implementation of existing methods.
bde is a Python package for Bayesian Deep Ensembles on tabular data, using MILE for efficient MCMC sampling and uncertainty quantification in regression and classification.
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.