# Handling Concept Drift via Model Reuse

arXiv: https://arxiv.org/abs/1809.02804 · HTML: https://mlsanity.com/paper/1809.02804/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2018.

## Built on by (most cited first, 15 of 15)

- [Open-environment Machine Learning](https://mlsanity.com/paper/2206.00423/) (2022)
- [DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation](https://mlsanity.com/paper/2201.04038/) (2022)
- [Adapting to Online Label Shift with Provable Guarantees](https://mlsanity.com/paper/2207.02121/) (2022)
- [Learnware: Small Models Do Big](https://mlsanity.com/paper/2210.03647/) (2022)
- [Beimingwu: A Learnware Dock System](https://mlsanity.com/paper/2401.14427/) (2024)
- [MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation](https://mlsanity.com/paper/2309.14216/) (2023)
- [MRCpy: A Library for Minimax Risk Classifiers](https://mlsanity.com/paper/2108.01952/) (2021)
- [From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach](https://mlsanity.com/paper/2207.11742/) (2022)
- [Hypothesis Transfer in Bandits by Weighted Models](https://mlsanity.com/paper/2211.07387/) (2022)
- [Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees](https://mlsanity.com/paper/2310.15974/) (2023)
- [A Scalable Approach to Covariate and Concept Drift Management via Adaptive Data Segmentation](https://mlsanity.com/paper/2411.15616/) (2024)
- [Source Component Shift Adaptation via Offline Decomposition and Online Mixing Approach](https://mlsanity.com/paper/2508.10257/) (2025)
- [Agile Online Model Selection: Resolving Adaptation Lag via Safeguarded Large Learning Rates](https://mlsanity.com/paper/2605.26919/) (2026)
- [DT-GOL: Dual-Track Geometric Online Learning in Nonstationary Environment with Label Delay](https://mlsanity.com/paper/2606.22950/) (2026)
- [Learnware and AI Model Management System](https://mlsanity.com/paper/2609.11656/) (2026)

## 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.