arXiv cs.AI· Mahmoud Sanad, Mohammed Bakr Sikal, Osama Abboud, Heba Khdr, J\"org Henkel·· 10 小时前AI 评分20
LEX：面向 OOD 鲁棒运行时预测的 XAI 驱动置信度估计框架
XAI-Driven Confidence Estimation for OOD-Resilient Runtime Prediction
AI 导读
研究者提出 Lightweight Runtime EXplainability（LEX），首个用于 ML 资源管理模型的事后 XAI 框架，可在不重训练的情况下为每个输入给出细粒度运行时置信度。
正文 · 原文
Abstract:Machine learning-based resource management (RM) models degrade under out-of-distribution (OOD) inputs at runtime, yet existing approaches in the literature rely on a single global error margin that is overly conservative and fails to distinguish reliablepredictions from catastrophic failures. We propose Lightweight Runtime EXplainability (LEX), the first post-hoc XAI framework for input-specific runtime confidence estimation in ML-based RM. LEX clusters the embeddings of the training input space using a Gaussian Mixture Model and associates per-cluster prediction error bounds to input samples, enabling fine-grained runtime confidence signals without retraining. Evaluated on a state-of-the-art LSTM-based IPS forecasting model on Intel Alder Lake, across 34 benchmark applications, LEX achieves ~94% containment on unseen workloads and produces ~17% tighter per-input confidence bounds than global baselines. With its negligible runtime overhead, LEX advances the field by providing explainable and reliable input-specific confidence signals of ML predictions, thereby making ML-based resource management resilient to OOD at runtime.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.13199 [cs.LG] |
| (or arXiv:2611.13199v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13199 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mahmoud Sanad [view email]
[v1]
Mon, 9 Nov 2026 20:23:12 UTC (3,391 KB)
来源：arXiv cs.AI · arxiv.org