Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences

arXiv:2403.19871v1 Announce Type: new
Abstract: Retraining machine learning models remains an important task for real-world machine learning model deployment. Existing methods focus largely on greedy approaches to find the best-performing model without considering the stability of trained model structures across different retraining evolutions. In this study, we develop a mixed integer optimization algorithm that holistically considers the problem of retraining machine learning models across different data batch updates. Our method focuses on retaining consistent analytical insights - which is important …

abstract arxiv cs.ai cs.lg deployment focus machine machine learning machine learning model machine learning models math.oc mixed model deployment model retraining retraining stability study type via world