Data is a central component of machine learning and causal inference tasks.
The availability of large amounts of data from sources such as open data
repositories, data lakes and data marketplaces creates an opportunity to
augment data and boost those tasks' performance. However, augmentation
techniques rely on a user manually discovering and shortlisting useful
candidate augmentations. Existing solutions do not leverage the synergy between
discovery and augmentation, thus under exploiting data.
In this paper, we introduce METAM, a novel goal-oriented framework that
queries the downstream task with a candidate dataset, forming a feedback loop
that automatically steers the discovery and augmentation process. To select
candidates efficiently, METAM leverages properties of the: i) data, ii) utility
function, and iii) solution set size. We show METAM's theoretical guarantees
and demonstrate those empirically on a broad set of tasks. All in all, we
demonstrate the promise of goal-oriented data discovery to modern data science
applications.

本文介绍了一种名为 METAM 的目标导向框架，该框架可自动引导发现与扩充数据，并通过从下游任务中查询候选数据集来选择最佳候选方法，理论保证，并在广泛的任务集合上进行了实证证明，证明了目标导向数据发现对现代数据科学应用的潜力。