bde: A Python Package for Bayesian Deep Ensembles via MILE

• URL: http://arxiv.org/abs/2605.14146v1
• Date: Wed, 13 May 2026 21:52:49 GMT
• Title: bde: A Python Package for Bayesian Deep Ensembles via MILE
• Abstract summary: bde is built on an efficient JAX implementation of the sampling-based inference method Microcanonical Langevin Ensembles (MILE)<n>It provides scikit-learn compatible estimators for fast training, efficient Markov Chain Monte Carlo sampling, and uncertainty quantification in both regression and classification tasks.
• Score: 12.743164461113695
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• SCPP: A Unified Python Library for Soft Clustering [0.0]
  SCPP establishes a scikit-learn-compatible estimator interface that standardizes model training, prediction, membership representation, evaluation, and benchmarking.<n>The framework integrates 40 representative algorithms together with a comprehensive benchmarking comprising datasets, clustering quality metrics, and standardized runtime, memory, and scalability evaluation.
  arXiv  Detail & Related papers  (2026-07-21T23:01:38Z)
• milearn: A Python Package for Multi-Instance Machine Learning [0.0]
  milearn is a Python package for multi-instance learning (MIL)<n>Special emphasis is placed on the key instance detection (KID) problem.
  arXiv  Detail & Related papers  (2025-12-01T05:15:28Z)
• Efficient Solvers for SLOPE in R, Python, Julia, and C++ [5.542449901887863]
  We present a suite of packages that efficiently solve the Sorted L-One Penalized Estimation problem.<n>The packages feature a highly efficient hybrid coordinate descent algorithm that fits generalized linear models.<n>Our implementation is designed to be fast, memory-efficient, and flexible.
  arXiv  Detail & Related papers  (2025-11-04T10:03:15Z)
• CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training [63.07024608399447]
  We propose an automated framework that discovers, evaluates, and refines data mixtures in a pre-training setting.<n>We introduce ClimbLab, a filtered 1.2-trillion-token corpus with 20 clusters as a research playground, and ClimbMix, a compact yet powerful 400-billion-token dataset.
  arXiv  Detail & Related papers  (2025-04-17T17:58:13Z)
• Simulation-based inference with the Python Package sbijax [0.7499722271664147]
  sbijax is a Python package that implements a wide variety of state-of-the-art methods in neural simulation-based inference. The package provides functionality for approximate Bayesian computation, to compute model diagnostics, and to automatically estimate summary statistics.
  arXiv  Detail & Related papers  (2024-09-28T18:47:13Z)
• eipy: An Open-Source Python Package for Multi-modal Data Integration using Heterogeneous Ensembles [2.957103424179249]
  eipy is an open-source Python package for developing effective, multi-modal heterogeneous ensembles for classification.<n>eipy provides both a rigorous, and user-friendly framework for comparing and selecting the best-performing data integration and predictive modeling methods.
  arXiv  Detail & Related papers  (2024-01-17T20:07:47Z)
• LCE: An Augmented Combination of Bagging and Boosting in Python [45.65284933207566]
  lcensemble is a high-performing, scalable and user-friendly Python package for the general tasks of classification and regression. Local Cascade Ensemble (LCE) is a machine learning method that further enhances the prediction performance of the current state-of-the-art methods Random Forest and XGBoost.
  arXiv  Detail & Related papers  (2023-08-14T16:34:47Z)
• Latte: Cross-framework Python Package for Evaluation of Latent-Based Generative Models [65.51757376525798]
  Latte is a Python library for evaluation of latent-based generative models. Latte is compatible with both PyTorch and/Keras, and provides both functional and modular APIs.
  arXiv  Detail & Related papers  (2021-12-20T16:00:28Z)
• Transformers Can Do Bayesian Inference [56.99390658880008]
  We present Prior-Data Fitted Networks (PFNs) PFNs leverage in-context learning in large-scale machine learning techniques to approximate a large set of posteriors. We demonstrate that PFNs can near-perfectly mimic Gaussian processes and also enable efficient Bayesian inference for intractable problems.
  arXiv  Detail & Related papers  (2021-12-20T13:07:39Z)
• Scikit-dimension: a Python package for intrinsic dimension estimation [58.8599521537]
  This technical note introduces textttscikit-dimension, an open-source Python package for intrinsic dimension estimation. textttscikit-dimension package provides a uniform implementation of most of the known ID estimators based on scikit-learn application programming interface. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation in real-life and synthetic data.
  arXiv  Detail & Related papers  (2021-09-06T16:46:38Z)
• Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python [77.33905890197269]
  We describe a new library which implements a unified pathwise coordinate optimization for a variety of sparse learning problems. The library is coded in R++ and has user-friendly sparse experiments.
  arXiv  Detail & Related papers  (2020-06-27T02:39:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.