Learning from data with structured missingness

Mitra, R. et al. (2023) Learning from data with structured missingness. Nature Machine Intelligence, 5, pp. 13-23. (doi: 10.1038/s42256-022-00596-z)

Abstract

Missing data are an unavoidable complication in many machine learning tasks. When data are ‘missing at random’ there exist a range of tools and techniques to deal with the issue. However, as machine learning studies become more ambitious, and seek to learn from ever-larger volumes of heterogeneous data, an increasingly encountered problem arises in which missing values exhibit an association or structure, either explicitly or implicitly. Such ‘structured missingness’ raises a range of challenges that have not yet been systematically addressed, and presents a fundamental hindrance to machine learning at scale. Here we outline the current literature and propose a set of grand challenges in learning from data with structured missingness.

• Item Type: Articles
• Additional Information: This work was sponsored by the Turing-Roche Strategic Partnership.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Basiri, Professor Ana
• Authors: Mitra, R., McGough, S. F., Chakraborti, T., Holmes, C., Copping, R., Hagenbuch, N., Biedermann, S., Noonan, J., Lehmann, B., Shenvi, A., Vinh Doan, X., Leslie, D., Bianconi, G., Sanchez-Garcia, R., Davies, A., Macintosh, M., Andrinopoulou, E.-R., Basiri, A., Harbron, C., and MacArthur, B. D.
• College/School: College of Science and Engineering > School of Geographical and Earth Sciences
• Journal Name: Nature Machine Intelligence
• Publisher: Nature Research
• ISSN: 2522-5839
• ISSN (Online): 2524-4914
• Published Online: 25 January 2023
• Copyright Holders: Copyright © 2023 Springer Nature
• First Published: First published in Nature Machine Intelligence 5:13-23
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher