arXiv:2606. 07789v1 Announce Type: new Abstract: Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance.
By Vitor Cerqueira, Heitor Murilo Gomes, Marco Heyden, Bernhard Pfahringer, Albert Bifet
arXiv:2606. 20216v1 Announce Type: cross Abstract: Machine learning algorithms deployed for evolving streaming environments must handle the non-stationary data distributions, commonly referred to as concept drift.
By Md Moman Ul Haque Khan, Samira Sadaoui
arXiv:2608. 19488v1 Announce Type: new Abstract: Production machine learning systems degrade under concept drift, yet practitioners have little principled guidance on when to retrain.
By Sawan Dasari
AdaptLSTM is an adaptive online learning framework designed for cloud workload forecasting under distribution drift. It detects drift using validation‑calibrated thresholds and performs selective, targeted updates, achieving a large portion of the accuracy gains of naive online learning while reducing per‑step compute cost. Experiments on Alibaba and Container traces show significant efficiency improvements and error reductions compared to static models and classical drift detectors.
By Xinhua Miao, Bowei Yang, Zhengong Cai
arXiv:2610.17399v1 Announce Type: cross
Abstract: Continuous monitoring of generative AI systems requires detecting distribution shifts in the unstructured data they ingest and generate. Existing mon...
By Siyu Zhou, Jialin Ouyang, Fan Xia, Adarsh Subbaswamy, Drew Prinster, Suchi Saria, Augusto Garcia-Agundez, Sarah Pollet, Jinoos Yazdany, Patrick Vossler, Jean Feng
The paper introduces AIGS, a lightweight Adaptive Incremental Gating System designed for online representation learning in non‑stationary data streams. AIGS uses a Shock Ratio feedback signal to drive a Continuous Plasticity Controller, enabling smooth adaptation between learning plasticity and memory retention while keeping per‑step complexity linear in the feature dimension. Experiments on smart‑city traffic, meteorological, and industrial datasets show that AIGS improves early‑warning lead times, accelerates recovery after abrupt changes, and enhances anomaly recall in noisy environments.
By SiRui He, Kai Liang Lew, Chui Zi Ong, Chean Khim Toa