Cover Image

bde: A Python Package for Bayesian Deep Ensembles via MILE

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Arvanitis, Vyron, Aslanidis, Angelos, Sommer, Emanuel, Rügamer, David
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning
by: Sommer, Emanuel, et al.
Published: (2026)

Towards E-Value Based Stopping Rules for Bayesian Deep Ensembles
by: Sommer, Emanuel, et al.
Published: (2026)

Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks
by: Sommer, Emanuel, et al.
Published: (2025)

On the Interplay of Priors and Overparametrization in Bayesian Neural Network Posteriors
by: Kobialka, Julius, et al.
Published: (2026)

Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
by: Sommer, Emanuel, et al.
Published: (2024)

Can Microcanonical Langevin Dynamics Leverage Mini-Batch Gradient Noise?
by: Sommer, Emanuel, et al.
Published: (2026)

Paths and Ambient Spaces in Neural Loss Landscapes
by: Dold, Daniel, et al.
Published: (2025)

NUBO: A Transparent Python Package for Bayesian Optimization
by: Diessner, Mike, et al.
Published: (2023)

BARMPy: Bayesian Additive Regression Models Python Package
by: Van Boxel, Danielle
Published: (2024)

Scalable Higher-Order Tensor Product Spline Models
by: Rügamer, David
Published: (2024)

Bayesian Semi-structured Subspace Inference
by: Dold, Daniel, et al.
Published: (2024)

MILE: Model-based Intervention Learning
by: Korkmaz, Yigit, et al.
Published: (2025)

eipy: An Open-Source Python Package for Multi-modal Data Integration using Heterogeneous Ensembles
by: Bennett, Jamie J. R., et al.
Published: (2024)

On the Epistemic Uncertainty of Overparametrized Neural Networks
by: Rügamer, David
Published: (2026)

On Uniform, Bayesian, and PAC-Bayesian Deep Ensembles
by: Hauptvogel, Nick, et al.
Published: (2024)

Calibrating LLMs with Information-Theoretic Evidential Deep Learning
by: Li, Yawei, et al.
Published: (2025)

Can Transformers Learn Full Bayesian Inference in Context?
by: Reuter, Arik, et al.
Published: (2025)

Rethinking Intrinsic Dimension Estimation in Neural Representations
by: Schulte, Rickmer, et al.
Published: (2026)

Additive Model Boosting: New Insights and Path(ologie)s
by: Schulte, Rickmer, et al.
Published: (2025)

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries
by: Kolb, Chris, et al.
Published: (2025)

dnamite: A Python Package for Neural Additive Models
by: Van Ness, Mike, et al.
Published: (2025)

DeepOnto: A Python Package for Ontology Engineering with Deep Learning
by: He, Yuan, et al.
Published: (2023)

Position: The Future of Bayesian Prediction Is Prior-Fitted
by: Müller, Samuel, et al.
Published: (2025)

OGBoost: A Python Package for Ordinal Gradient Boosting
by: Sharabiani, Mansour T. A., et al.
Published: (2025)

milearn: A Python Package for Multi-Instance Machine Learning
by: Zankov, Dmitry, et al.
Published: (2025)

Simulation-based Inference with the Python Package sbijax
by: Dirmeier, Simon, et al.
Published: (2024)

Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects
by: Köhler, David, et al.
Published: (2024)

Differentiable Sparsity via $D$-Gating: Simple and Versatile Structured Penalization
by: Kolb, Chris, et al.
Published: (2025)

Enhancing Heavy Rain Nowcasting with Multimodal Data: Integrating Radar and Satellite Observations
by: Kassoumeh, Rama, et al.
Published: (2025)

MILE: A Mutation Testing Framework of In-Context Learning Systems
by: Wei, Zeming, et al.
Published: (2024)

HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs
by: Zhang, Yue, et al.
Published: (2024)

Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors
by: Nagler, Thomas, et al.
Published: (2025)

RobPy: a Python Package for Robust Statistical Methods
by: Leyder, Sarah, et al.
Published: (2024)

UQLM: A Python Package for Uncertainty Quantification in Large Language Models
by: Bouchard, Dylan, et al.
Published: (2025)

hyppo: A Multivariate Hypothesis Testing Python Package
by: Panda, Sambit, et al.
Published: (2019)

scikit-fda: A Python Package for Functional Data Analysis
by: Ramos-Carreño, Carlos, et al.
Published: (2022)

LLM-IE: A Python Package for Generative Information Extraction with Large Language Models
by: Hsu, Enshuo, et al.
Published: (2024)

regAL: Python Package for Active Learning of Regression Problems
by: Surzhikova, Elizaveta, et al.
Published: (2024)

Epistemic Uncertainty-aware Recommendation Systems via Bayesian Deep Ensemble Learning
by: Cheraghi, Radin, et al.
Published: (2025)

TorchSurv: A Lightweight Package for Deep Survival Analysis
by: Monod, Mélodie, et al.
Published: (2024)