论文标题
机器学习的数据预算
Data Budgeting for Machine Learning
论文作者
论文摘要
数据是为AI供电的燃料，并为许多域创造了巨大的价值。但是，为AI收集数据集是一项耗时，昂贵且复杂的努力。对于从业者来说，数据投资仍然是实践中信仰的飞跃。在这项工作中，我们研究数据预算问题并将其作为两个子问题提出：预测（1）如果给出足够的数据，则饱和性能是什么，以及（2）在饱和性能附近需要多少数据点。与传统的与数据集无关的方法（如PowerLaw）不同，我们提出了一种解决数据预算问题的学习方法。为了支持和系统地评估基于学习的数据预算方法，我们策划了大量383个表格ML数据集的集合，以及它们的数据与性能曲线。我们的经验评估表明，在一个小型试点研究数据集的情况下，可以执行数据预算，该数据集少于$ 50 $。
Data is the fuel powering AI and creates tremendous value for many domains. However, collecting datasets for AI is a time-consuming, expensive, and complicated endeavor. For practitioners, data investment remains to be a leap of faith in practice. In this work, we study the data budgeting problem and formulate it as two sub-problems: predicting (1) what is the saturating performance if given enough data, and (2) how many data points are needed to reach near the saturating performance. Different from traditional dataset-independent methods like PowerLaw, we proposed a learning method to solve data budgeting problems. To support and systematically evaluate the learning-based method for data budgeting, we curate a large collection of 383 tabular ML datasets, along with their data vs performance curves. Our empirical evaluation shows that it is possible to perform data budgeting given a small pilot study dataset with as few as $50$ data points.
