Generalized Bayesian Inference

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8 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Scalable AI Uncertainty Quantification via Generalized Laplace Active Subspaces
Oct 8, 2026Wouter N. Edeling, Peter V. CoveneyGeneralized Bayesian Inference Uncertainty Quantification
Scientific Computing Group, CWI, Science park 123, Amsterdam, 1098XG, the Netherlands. · Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Hallenweg 15, Enschede, 7500AE, the Netherlands. · Centre for Computational Science, University College London, 20 Gordon Street, London, WC1H 0AJ, UK. +1
A Generalized-Bayes Perspective on Counterfactual Explanations: Posterior-Based Decision-Making and Evaluation
Jul 31, 2026Keita KinjoGeneralized Bayesian Inference Counterfactual Evaluation
Faculty of Business, Kyoritsu Women’s University
Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors
May 18, 2026Aldric LabartheGeneralized Bayesian Inference Latent Variable Models
CB, UNIGE · Centre Borelli, Université Paris-Saclay Gif-Sur-Yvette, FR · Department of Computer Science, University of Geneva Carouge, CH-1227
Teacher Forcing as Generalized Bayes: Optimization Geometry Mismatch in Switching Surrogates for Chaotic Dynamics
Apr 28, 2026Andre Herz, Daniel Durstewitz, Georgia KoppeNeural Surrogate Modeling Generalized Bayesian Inference
Interdisciplinary Center for Scientific Computing, Heidelberg University, Germany · Dept. of Theoretical Neuroscience, Central Institute of Mental Health (CIMH), Mannheim, Germany · Hector Institute for AI in Psychiatry and Dept. of Psychiatry and Psychotherapy, CIMH, Mannheim, Germany +1
Generalized Bayes for Causal Inference
Mar 3, 2026Emil Javurek, Dennis Frauen, Yuxin Wang +1Generalized Bayesian Inference Causal Inference
LMU Munich · Munich Center for Machine Learning
General Bayesian Policy Learning
Feb 27, 2026Masahiro KatoGeneralized Bayesian Inference Policy Learning
1Mizuho-DL Financial Technology Co., Ltd., Tokyo, Japan
Robust Experimental Design via Generalised Bayesian Inference
Nov 10, 2025Yasir Zubayr Barlas, Sabina J. Sloman, Samuel KaskiGeneralized Bayesian Inference Bayesian Experimental Design
The University of Manchester · Aalto University
The Fenchel-Young Generalization of Bayesian Principles
Feb 14, 2025Sophia Sklaviadis, Thomas Moellenhoff, Emtiyaz Khan +2Generalized Bayesian Inference Bayesian Inference
Instituto de Telecomunicacoes, and Instituto Superior Técnico, Lisbon, Portugal · RIKEN AIP

Oct 8, 2026J/K move · Enter open · S save

Scalable AI Uncertainty Quantification via Generalized Laplace Active Subspaces

Wouter N. Edeling, Peter V. Coveney

Scientific Computing Group, CWI, Science park 123, Amsterdam, 1098XG, the Netherlands. · Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Hallenweg 15, Enschede, 7500AE, the Netherlands. · Centre for Computational Science, University College London, 20 Gordon Street, London, WC1H 0AJ, UK.+1

Generalized Bayesian Inference Uncertainty Quantification Uncertainty Calibration Bayesian Inference

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Reliable uncertainty quantification (UQ) is essential for deploying neural networks in scientific and high-stakes applications, but full Bayesian inference over the network parameters is computationally infeasible. We propose a low-rank generalized Laplace approximation for neural-network UQ based on a small number of data-informed curvature directions. Starting from a generalized Bayesian posterior defined through an empirical loss, we construct a local Gaussian approximation around a pretrained set of weights in this active curvature subspace. The posterior variances in the retained subspace are available in closed form, and the prior variance is calibrated by an empirical Bayes procedure. The generalized Bayesian formulation allows us to compare two posterior scalings: the standard Bayesian scaling associated with the summed negative log likelihood, and a mean-loss scaling in which the empirical loss is normalized by the number of data. A central finding is that the standard scaling induces a data-size dependent contraction of the posterior variance in the leading active directions. In regression problems, this can force the low-rank framework to retain additional weak-curvature directions in order to achieve nominal coverage of calibration data. When posterior samples are propagated through the non-linear network, these additional directions can degrade the coherence of the predictive intervals and shift the posterior predictive mean away from the pretrained model. In contrast, the generalized mean-loss scaling yields a more stable, lower dimensional active subspace and produces calibrated, coherent predictive confidence intervals. These results indicate that generalized Laplace active subspaces provide a practical and scalable route to calibrated uncertainty quantification in neural networks.