Mitra, R;
McGough, SF;
Chakraborti, T;
Holmes, C;
Copping, R;
Hagenbuch, N;
Biedermann, S;
... MacArthur, BD; + view all
(2023)
Learning from data with structured missingness.
Nature Machine Intelligence
, 5
pp. 13-23.
10.1038/s42256-022-00596-z.
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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.
| Type: | Article |
|---|---|
| Title: | Learning from data with structured missingness |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s42256-022-00596-z |
| Publisher version: | https://doi.org/10.1038/s42256-022-00596-z |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Interdisciplinary Applications, Computer Science, MULTIPLE IMPUTATION, INFERENCE |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10166739 |
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