Bayesian Model Averaging

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8 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?
Oct 9, 2026Manuel Hülskamp, Julius Kobialka, Emanuel Sommer +2Bayesian Model Averaging Prior-Data Fitted Networks
LMU Munich and Munich Center for Machine Learning
Bayesian Entropy-based Reordering for Calibrated Diffusion Language Models
Oct 4, 2026Zhejun Jiang, Mijung ParkConfidence Estimation in Language Models Bayesian Inference
The University of British Columbia
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
Regional summaries of Bayesian model uncertainty
Jun 19, 2026Hanqing Li, Robert Nguyen, Xuewen LuBayesian Inference Bayesian Model Averaging
Department of Mathematics and Statistics University of Calgary Calgary, AB T2N 1N4, Canada
Sample Complexity and Decision-Theoretic Guarantees for Bayesian Model Averaging over Decision Trees with Catalan-Exponential Priors
May 31, 2026Livija Jakaite, Vitaly SchetininSample Complexity Bayesian Inference
School of Computing and Engineering University of Bedfordshire, Luton, UK
On the Construction and Implications of Low-Loss Valleys in LoRA-based Bayesian Inference
May 28, 2026Daniel Dold, Emanuel Sommer, Julius Kobialka +2Mode Connectivity Bayesian Inference
HTWG Konstanz · LMU Munich · Munich Center for Machine Learning (MCML)
Adapting Point Cloud Analysis via Multimodal Bayesian Distribution Learning
Mar 23, 2026Xingyu Zhu, Liang Yi, Shuo Wang +4Bayesian Model Averaging Test-Time Adaptation of VLMs
MoE Key Lab of BIPC, University of Science and Technology of China · Opus AI Research · Southeast University +1
PREBA: Surgical Duration Prediction via PCA-Weighted Retrieval-Augmented LLMs and Bayesian Averaging Aggregation
Feb 27, 2026Wanyin Wu, Kanxue Li, Baosheng Yu +4Bayesian Model Averaging Clinical Prediction
Wanyin Wu, Haoyun Zhao, and Dapeng Tao are with the School of Information Science and Engineering, Yunnan University, Kunming 650500, China. · Kanxue Li and Yibing Zhan are with the School of Computer Science, Wuhan University, Wuhan 430072, China. · Baosheng Yu is with the Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 639798. +1

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How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?

Manuel Hülskamp, Julius Kobialka, Emanuel Sommer, Thomas Nagler+1

LMU Munich and Munich Center for Machine Learning

Bayesian Model Averaging Prior-Data Fitted Networks Amortized Inference Bayesian Inference

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Prior-data fitted networks (PFNs) are trained to approximate the Bayesian posterior predictive distribution (PPD) of a synthetic prior, yet results about the approximation quality are surprisingly thin. Taking an empirical risk minimization perspective, we decompose the deployment error of PFNs into prior, context, and amortization error, and measure the latter against a reference PPD under a realistic prior. Results show that PFNs generally recover the targeted PPD very well. Nevertheless, we find that parts of the residual amortization error persist, especially in rare settings under the prior pretraining distribution, even when scaling the model size, which manifests in disagreement between independently pretrained PFNs and leaves uncertainty about the predictor at inference time. As a remedy, we study multiple types of Bayesian model averages and find that these can consistently reduce this uncertainty and improve predictions up to full-size TabICLv2 models.