mlsanityData to 7 Oct 2026

Handling Concept Drift via Model Reuse

arXiv 1809.02804 ↗First posted 8 Sep 2018cs.LG cs.AI stat.MLcited by 15

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Abstract (arXiv)

In many real-world applications, data are often collected in the form of stream, and thus the distribution usually changes in nature, which is referred as concept drift in literature. We propose a novel and effective approach to handle concept drift via model reuse, leveraging previous knowledge by reusing models. Each model is associated with a weight representing its reusability towards current data, and the weight is adaptively adjusted according to the model performance. We provide generalization and regret analysis. Experimental results also validate the superiority of our approach on both synthetic and real-world datasets.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Sep 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 6; 2023: 2; 2024: 2; 2025: 1; 2026: 3.

15 papers.

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  1. 2022Open-environment Machine Learningcited by 48
  2. 2022DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptationcited by 36
  3. 2022Adapting to Online Label Shift with Provable Guaranteescited by 26
  4. 2022Learnware: Small Models Do Bigcited by 16
  5. 2024Beimingwu: A Learnware Dock Systemcited by 8
  6. 2023MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptationcited by 6
  7. 2021MRCpy: A Library for Minimax Risk Classifierscited by 5
  8. 2022From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approachcited by 3
  9. 2022Hypothesis Transfer in Bandits by Weighted Modelscited by 2
  10. 2023Minimax Forward and Backward Learning of Evolving Tasks with Performance Guaranteescited by 2
  11. 2024A Scalable Approach to Covariate and Concept Drift Management via Adaptive Data Segmentationcited by 2
  12. 2025Source Component Shift Adaptation via Offline Decomposition and Online Mixing Approachcited by 0
3 more
  1. 2026Agile Online Model Selection: Resolving Adaptation Lag via Safeguarded Large Learning Ratescited by 0
  2. 2026DT-GOL: Dual-Track Geometric Online Learning in Nonstationary Environment with Label Delaycited by 0
  3. 2026Learnware and AI Model Management Systemcited by 0