Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
<|reference_start|>Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences: We consider the task of retraining machine learning (ML) models when new batches of data become available. Existing methods focus largely on greedy approaches to find the best-performing model for each batch, without considering the stability of the model's structure across retraining iterations. In this study, we propose a methodology for finding sequences of ML models that are stable across retraining iterations. We develop a mixed-integer optimization formulation that is guaranteed to recover Pareto optimal models (in terms of the predictive power-stability trade-off) and an efficient polynomial-time algorithm that performs well in practice. We focus on retaining consistent analytical insights - which is important to model interpretability, ease of implementation, and fostering trust with users - by using custom-defined distance metrics that can be directly incorporated into the optimization problem. Our method shows stronger stability than greedily trained models with a small, controllable sacrifice in predictive power, as evidenced through a real-world case study in a major hospital system in Connecticut.<|reference_end|>
@article{bertsimas2024towards,
    title={Towards Stable Machine Learning Model Retraining via Slowly Varying
  Sequences},
    author={Dimitris Bertsimas, Vassilis Digalakis Jr, Yu Ma, Phevos Paschalidis},
    journal={arXiv preprint arXiv:2403.19871},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19871},
    primaryClass={cs.LG cs.AI math.OC}
}