TRACE: A Generalizable Drift Detector for Streaming Data-Driven Optimization
Yuan-Ting Zhong, Ting Huang, Xiaolin Xiao, Yue-Jiao Gong
Abstract
Many optimization tasks involve streaming data with unknown concept drifts, posing a significant challenge as Streaming Data-Driven Optimization (SDDO). Existing methods, while leveraging surrogate model approximation and historical knowledge transfer, are often under restrictive assumptions such as fixed drift intervals and fully environmental observability, limiting their adaptability to diverse dynamic environments. We propose TRACE, a TRAnsferable Concept-drift Estimator that effectively detects distributional changes in streaming data with varying time scales. TRACE leverages a principled tokenization strategy to extract statistical features from data streams and models drift patterns using attention-based sequence learning, enabling accurate detection on unseen datasets and highlighting the transferability of learned drift patterns. Further, we showcase TRACE
BibTeX
@inproceedings{aaai2026_traceageneraliza,
title = {TRACE: A Generalizable Drift Detector for Streaming Data-Driven Optimization},
author = {Yuan-Ting Zhong and Ting Huang and Xiaolin Xiao and Yue-Jiao Gong},
booktitle = {AAAI 2026},
year = {2026}
}