Related papers: METAM: Goal-Oriented Data Discovery

METAM: Goal-Oriented Data Discovery

URL: http://arxiv.org/abs/2304.09068v1
Date: Tue, 18 Apr 2023 15:42:25 GMT
Title: METAM: Goal-Oriented Data Discovery
Authors: Sainyam Galhotra and Yue Gong and Raul Castro Fernandez
Abstract summary: METAM is a goal-oriented framework that queries the downstream task with a candidate dataset, forming a feedback loop that automatically steers the discovery and augmentation process. We show METAM's theoretical guarantees and demonstrate those empirically on a broad set of tasks.
Score: 9.73435089036831
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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