We introduce an efficient Bayesian deep ensemble method for predictive regression designed to enhance interpretability while maintaining competitive predictive performance and computational efficiency. Our method combines the statistical rigor of Bayesian inference with the scalability of deep ensembles, providing calibrated uncertainty estimates that enable its use not only for standalone prediction but also as a component within broader learning systems.
The paper introduces a method for training sparse Bayesian neural networks by integrating spike-and-slab priors into sticky piecewise deterministic Markov processes (PDMPs). It demonstrates that these modern Monte Carlo techniques can sample posterior distributions of Bayesian neural networks exactly, provided numerical rate bounds are satisfied. Experiments show that the resulting sparse models not only converge well but also exhibit greater robustness to input noise compared to their dense counterparts.
By August Arnstad, Geir Olve Storvik, Filippo Pagani, Leiv R{\o}nneberg
arXiv:2607. 06776v1 Announce Type: new Abstract: We introduce an efficient Bayesian deep ensemble method for predictive regression designed to enhance interpretability while maintaining competitive predictive performance and computational efficiency.
By Sina Aghaee Dabaghan Fard, Marie Maros, Jaesung Lee
arXiv:2601. 07944v2 Announce Type: replace-cross Abstract: Since the turn of the century, approximate Bayesian inference has steadily evolved as new computational techniques have been incorporated to handle increasingly complex, large-scale predictive problems.
By Roy Shivam Ram Shreshtth, Arnab Hazra, Gourab Mukherjee
arXiv:2610.19074v1 Announce Type: new
Abstract: Reliable uncertainty quantification remains an important challenge in AI. Hierarchical Bayesian methods provide a principled framework for this by repr...
By Neil K. Chada, Simon Cotter, Kody J. H. Law, Xinzhu Liang
arXiv:2606. 01468v1 Announce Type: cross Abstract: Due to their explicit priors and ability to model uncertainty, Bayesian methods have played a major role in dynamical latent variable modeling of single-cell neural recordings.
By JR Huml, Jonathan Wenger, John P. Cunningham
arXiv:2609.39525v1 Announce Type: new
Abstract: Casting Bayesian inference as a neural network optimization problem targeting an amortized posterior is attractive, as it extends to otherwise intracta...
By Hans Olischl\"ager, Svenja Jedhoff, \v{S}imon Kucharsk\'y, Aayush Mishra, Stefan T. Radev, Paul B\"urkner
The paper introduces Federated Martingale Posterior (FMP) sampling, a one‑shot, embarrassingly parallel method for federated Bayesian neural networks. FMP replaces the traditional prior–likelihood framework with a predictive distribution, allowing each client to upload only a small set of trainable data embeddings while the server performs predictive sampling centrally. Experiments demonstrate that FMP closely matches the performance of a centralized approach and achieves the lowest mean expected calibration error among evaluated one‑shot federated methods.
By Boning Zhang, Matteo Zecchin, Mingzhao Guo, Dongzhu Liu, Osvaldo Simeone
arXiv:2512. 22999v2 Announce Type: replace-cross Abstract: We consider problems of parameter estimation where design variables can be actively optimized to maximize information gain.
By Niels Bracher, Lars K\"uhmichel, Desi R. Ivanova, Xavier Intes, Paul-Christian B\"urkner, Stefan T. Radev
The paper presents a pipeline for training, compiling, and deploying Bayesian neural networks (BNNs) that use a single probabilistic forward pass (PFP) instead of multiple stochastic samples. By assuming Gaussian-distributed weights and activations, PFP enables fully analytic uncertainty propagation, allowing a single deterministic forward pass to replace sampling. Using the TVM compiler and a library of Gaussian-propagating operators, the authors achieve up to 4200× speedups on small mini-batches while maintaining accuracy, uncertainty estimation, and out-of-domain detection comparable to traditional stochastic variational inference (SVI).
By Bernhard Klein, Falk Selker, Hendrik Borras, Sophie Steger, Franz Pernkopf, Holger Fr\"oning
arXiv:2606. 13818v1 Announce Type: new Abstract: This thesis investigates how Bayesian principles can deepen our understanding of modern deep learning systems.
By Luis A. Ortega
arXiv:2608. 02229v1 Announce Type: new Abstract: Classical neural networks frequently produce overconfident predictions on ambiguous or out-of-distribution (OOD) data, a liability that grows with each AI system deployed in safety-critical real-world scenarios.
By Hendrik Borras, Xiao Wang, Bernhard Klein, Robin Janssen, Frank Br\"uckerhoff-Pl\"uckelmann, Wolfram Pernice, Holger Fr\"oning