bde: A Python Package for Bayesian Deep Ensembles via MILE

TL;DR

bde is a Python package that enables Bayesian Deep Ensembles for tabular data using MILE, offering efficient sampling, training, and uncertainty quantification.

Contribution

It introduces a user-friendly Python package built on JAX for Bayesian Deep Ensembles with MILE, optimized for tabular data.

Findings

Provides fast training and sampling for Bayesian ensembles.

Enables uncertainty quantification in regression and classification.

Built with scikit-learn compatibility for ease of use.

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.