# Post-Hoc Uncertainty-Aware Explanations for Deployed Power Quality Disturbance Classifiers via Laplace Approximation

arXiv: https://arxiv.org/abs/2604.13658 · HTML: https://mlsanity.com/paper/2604.13658/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 15 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [A Simple Baseline for Bayesian Uncertainty in Deep Learning](https://mlsanity.com/paper/1902.02476/) (2019)
- [Weight Uncertainty in Neural Networks](https://mlsanity.com/paper/1505.05424/) (2015)
- [Evaluating the visualization of what a Deep Neural Network has learned](https://mlsanity.com/paper/1509.06321/) (2015)
- [Laplace Redux -- Effortless Bayesian Deep Learning](https://mlsanity.com/paper/2106.14806/) (2021)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI](https://mlsanity.com/paper/2003.07258/) (2020)
- [How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks](https://mlsanity.com/paper/2006.09000/) (2020)
- [Explaining Bayesian Neural Networks](https://mlsanity.com/paper/2108.10346/) (2021)
- [Generalized Linear Mode Connectivity for Transformers](https://mlsanity.com/paper/2506.22712/) (2025)
- [Model-agnostic variable importance for predictive uncertainty: an entropy-based approach](https://mlsanity.com/paper/2310.12842/) (2023)
- [Uncertainty Quantification for Gradient-based Explanations in Neural Networks](https://mlsanity.com/paper/2403.17224/) (2024)

## Abstract (arXiv)

Deep learning classifiers achieve high accuracy in power quality disturbance (PQD) recognition, but existing explanation methods return a single deterministic attribution map and provide no measure of its reliability. This paper develops a post-hoc Bayesian explanation (B-explanation) method for trained PQD classifiers. A computationally efficient Laplace approximation converts the trained network into an approximate parameter posterior without retraining, and occlusion sensitivity is propagated through posterior samples to produce a distribution over disturbance-localization maps. Percentile summaries of this distribution yield explanations with distribution-free coverage bands: consensus summaries at low percentiles sharpen localization significantly for distinctive events such as sags, swells, and oscillatory transients, the band width indicates the reliability of each attribution, and the remaining disturbance types show class-dependent behavior. Explanation dispersion also increases under injected measurement noise and synthetic-to-field transfer, complementing predictive uncertainty. Experiments on a synthetic benchmark of 15 disturbance classes and on field-recorded sags compare the method with Monte Carlo dropout and deep ensembles under a common evaluation protocol, evaluate it against deterministic occlusion, LIME, and SHAP with localization and faithfulness metrics, and characterize the computational cost of explanation generation for grid monitoring applications.