如何智能合并不同数据集，让预测模型更准且有理论保障
Collaborative Prediction: To Join or To Disjoin Datasets
- 基于正则化与数据驱动估计，设计可证明有效的数据融合算法
- 在标准线性回归和多种机器学习任务中显著降低总体损失
- 适合关注数据质量与模型泛化能力的研究者与工程师
随着生成式人工智能的兴起，选择高质量数据集以提升机器学习模型性能日益重要。然而，对于简单预测模型而言，该问题仍存在诸多未解之处。本文研究了如何设计实用算法，在高概率下最小化预测模型的总体损失。我们探讨了来自不同来源的数据集何时可有效合并以提升模型表现，并提出一种具备理论保证的实用算法。该算法利用奥拉克不等式与数据驱动估计器，显著降低总体损失。数值实验表明其在标准线性回归及更广泛的机器学习应用中均具有效性。代码已公开于 https://github.com/kkrokii/collaborative_prediction。
原文摘要 · Abstract (English)
With the recent rise of generative Artificial Intelligence (AI), the need of selecting high-quality dataset to improve machine learning models has garnered increasing attention. However, some part of this topic remains underexplored, even for simple prediction models. In this work, we study the problem of developing practical algorithms that select appropriate dataset to minimize population loss of our prediction model with high probability. Broadly speaking, we investigate when datasets from different sources can be effectively merged to enhance the predictive model's performance, and propose a practical algorithm with theoretical guarantees. By leveraging an oracle inequality and data-driven estimators, the algorithm reduces population loss with high probability. Numerical experiments demonstrate its effectiveness in both standard linear regression and broader machine learning applications. Code is available at https://github.com/kkrokii/collaborative_prediction.
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