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提出 PAI-KNN，一种通过推理时先验调整而非内存修改来适应概念漂移与类不平衡的 KNN 数据流分类器，在合成数据流基准上取得了与最先进方法竞争的性能。
This paper proposes a causal framework for understanding concept drift in data streams using Structural Causal Models, with a taxonomy and generator for simulating and evaluating drift events in non-stationary environments.
This paper proposes a two-stage integrated forecasting model using XGBoost to predict CPU workload in private clouds by first forecasting customer service requests, achieving high accuracy with SMAPE below 7% for most applications.
This paper introduces a provenance-guided incremental learning framework to handle rule-induced concept shifts, where target definitions change, and evaluates it on a new benchmark with improved efficiency and accuracy.
This paper empirically studies retraining policies for streaming machine learning systems under concept drift, budget, and latency constraints.
This paper introduces NOMADD, a post-hoc method to reduce concept drift in tabular models by fitting base models on labeled periods and extrapolating compressed parameter changes. It achieves competitive performance with Drift-Resilient TabPFN at a fraction of training and inference cost.
This paper provides a comprehensive survey of Federated Continual Learning (FCL), an emerging field that combines Federated Learning and Continual Learning to enable lifelong, adaptive, and privacy-preserving learning over distributed and non-stationary data. It proposes a taxonomy, reviews applications, metrics, and open challenges.
This paper investigates temporal concept drift in legal judgment prediction by fine-tuning transformer models on Ukrainian court decisions from three epochs defined by geopolitical disruptions. Findings show severe forward degradation, asymmetry in backward transfer, and that chronological continual learning effectively mitigates forgetting while domain pretraining reduces degradation magnitude.
This paper investigates disagreement-based drift detection in ensembles of incremental decision trees, finding that while effective in neural networks, the method underperforms loss-based detectors for tree ensembles due to limited model plasticity.