Title: Turning Uncertainty into Control: Bi-level Training with Editable Bayesian Layers
Primary area: interpretability and explainable AI
Keywords: Uncertainty Estimation, Interpretability

Abstract: As deep learning systems are increasingly deployed in high-stakes settings, it is essential not only to quantify predictive uncertainty but also to use it to steer training and improve downstream decision. Yet dense parameter updates often cause collateral interference due to the entangled nature of parameter. 
In this paper, We propose **B**ayesian **F**eature **R**eweighting (**BFR**), a framework that turns calibrated, instance-level uncertainty into training-time control signals while enforcing sparse, localized parameter updates. 
Concretely, BFR employs a Bayesian Last Layer to obtain well-calibrated predictive uncertainty with negligible overhead and imposes sparsity on the decision layer to concentrate changes on task-relevant parameters, thereby limiting unintended perturbations. It then tinkers the model via uncertainty-guide bi-level optimization. Across vision and language benchmarks, BFR consistently improves robustness while maintaining competitive accuracy and yields smaller parameter changes. Code is provided in the supplementary material.