通过缓慢变化序列实现稳定的机器学习模型重训练
Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
AI总结：
本文提出一个模型无关的框架，通过混合整数优化和自定义距离度量寻找跨重训练迭代稳定的模型序列，在预测能力仅降低2%的情况下实现稳定性提高30%。
摘要：
我们研究在新批次数据可用时重训练机器学习（ML）模型的问题。现有方法在每一批次上独立地贪婪优化预测能力，而不考虑模型结构的稳定性或跨重训练迭代的分析洞察。我们提出一个模型无关的框架，用于寻找在重训练迭代之间保持稳定的模型序列。我们开发了一种混合整数优化公式，保证能够恢复具有良好泛化性质的帕累托最优模型（就预测能力与稳定性的权衡而言），以及一种在实践中表现良好的高效多项式时间算法。我们专注于保留一致的分析洞察——这对模型可解释性、实现便利性以及培养用户信任很重要——通过使用可直接纳入优化问题的自定义距离度量。我们在多种模型（回归、决策树、提升树和神经网络）和应用领域（医疗、视觉和语言）上评估了我们的框架，包括在美国一家大型医院的生产流水线中部署。我们发现，平均而言，预测能力降低2%可带来稳定性提高30%。
英文摘要：
We consider the problem of retraining machine learning (ML) models when new batches of data become available. Existing approaches greedily optimize for predictive power independently at each batch, without considering the stability of the model's structure or analytical insights across retraining iterations. We propose a model-agnostic framework for finding sequences of 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) with good generalization properties, as well as 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. We evaluate our framework across models (regression, decision trees, boosted trees, and neural networks) and application domains (healthcare, vision, and language), including deployment in a production pipeline at a major US hospital. We find that, on average, a 2% reduction in predictive power leads to a 30% improvement in stability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
