用少量数据评估外部数据价值，让模型训练更高效
MYCROFT: Towards Effective and Efficient External Data Augmentation
- 基于特征距离与梯度匹配，筛选高价值小数据集
- 仅用少量数据就达到全量数据的性能水平
- 适合数据受限但需高效利用外部数据的场景
机器学习模型通常需要大量数据才能表现良好。当可用数据有限时，训练者可能需要从外部获取数据，但许多私有实体因隐私和产权顾虑不愿共享。为此，我们提出Mycroft，一种数据高效的方法，可在有限数据共享预算下评估不同数据源的相对价值。通过特征空间距离与梯度匹配，Mycroft能从每个数据所有者处识别出小而信息丰富的子集，使模型训练在最小数据暴露下实现最优性能。四个任务、两个领域的实验表明，Mycroft能快速收敛至全量数据基准性能，且对噪声具有鲁棒性，可有效按效用排序数据提供方。该方法有助于推动高性能模型的普惠化训练。
原文摘要 · Abstract (English)
Machine learning (ML) models often require large amounts of data to perform well. When the available data is limited, model trainers may need to acquire more data from external sources. Often, useful data is held by private entities who are hesitant to share their data due to propriety and privacy concerns. This makes it challenging and expensive for model trainers to acquire the data they need to improve model performance. To address this challenge, we propose Mycroft, a data-efficient method that enables model trainers to evaluate the relative utility of different data sources while working with a constrained data-sharing budget. By leveraging feature space distances and gradient matching, Mycroft identifies small but informative data subsets from each owner, allowing model trainers to maximize performance with minimal data exposure. Experimental results across four tasks in two domains show that Mycroft converges rapidly to the performance of the full-information baseline, where all data is shared. Moreover, Mycroft is robust to noise and can effectively rank data owners by utility. Mycroft can pave the way for democratized training of high performance ML models.
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