PrIU：一种基于种源的回归模型增量更新方法
PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models
摘要：
机器学习算法的广泛应用给传统的数据库问题如增量视图更新带来了新的挑战。人们正在努力更好地理解和调试机器学习模型，以及识别和修复训练数据集中的错误。我们的重点是如何帮助这些活动时，他们必须重新训练的机器学习模型后，在清理或选择不同的子集的训练数据的可解释性删除有问题的训练样本。本文提出了一种有效的基于种源的方法，PrIU，其优化版本，PrIU-opt，增量更新模型参数，而不牺牲预测精度。我们证明了增量更新模型参数的正确性和收敛性，并通过实验验证。实验结果表明，与简单地从头开始重新训练模型相比，PrIU-opt可以实现高达两个数量级的加速，但获得高度相似的模型。
英文摘要：
The ubiquitous use of machine learning algorithms brings new challenges to traditional database problems such as incremental view update. Much effort is being put in better understanding and debugging machine learning models, as well as in identifying and repairing errors in training datasets. Our focus is on how to assist these activities when they have to retrain the machine learning model after removing problematic training samples in cleaning or selecting different subsets of training data for interpretability. This paper presents an efficient provenance-based approach, PrIU, and its optimized version, PrIU-opt, for incrementally updating model parameters without sacrificing prediction accuracy. We prove the correctness and convergence of the incrementally updated model parameters, and validate it experimentally. Experimental results show that up to two orders of magnitude speed-ups can be achieved by PrIU-opt compared to simply retraining the model from scratch, yet obtaining highly similar models.
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