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Learning from data with structured missingness

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Learning from data with structured missingness. / Mitra, Robin; McGough, Sarah F.; Chakraborti, Tapabrata et al.
In: Nature Machine Intelligence, Vol. 5, No. 1, 25.01.2023, p. 13-23.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Mitra, R, McGough, SF, Chakraborti, T, Holmes, C, Copping, R, Hagenbuch, N, Biedermann, S, Noonan, J, Lehmann, B, Shenvi, A, Doan, XV, Leslie, D, Bianconi, G, Sanchez-Garcia, R, Davies, A, Mackintosh, M, Andrinopoulou, E-R, Basiri, A, Harbron, C & MacArthur, BD 2023, 'Learning from data with structured missingness', Nature Machine Intelligence, vol. 5, no. 1, pp. 13-23. https://doi.org/10.1038/s42256-022-00596-z

APA

Mitra, R., McGough, S. F., Chakraborti, T., Holmes, C., Copping, R., Hagenbuch, N., Biedermann, S., Noonan, J., Lehmann, B., Shenvi, A., Doan, X. V., Leslie, D., Bianconi, G., Sanchez-Garcia, R., Davies, A., Mackintosh, M., Andrinopoulou, E-R., Basiri, A., Harbron, C., & MacArthur, B. D. (2023). Learning from data with structured missingness. Nature Machine Intelligence, 5(1), 13-23. https://doi.org/10.1038/s42256-022-00596-z

Vancouver

Mitra R, McGough SF, Chakraborti T, Holmes C, Copping R, Hagenbuch N et al. Learning from data with structured missingness. Nature Machine Intelligence. 2023 Jan 25;5(1):13-23. doi: 10.1038/s42256-022-00596-z

Author

Mitra, Robin ; McGough, Sarah F. ; Chakraborti, Tapabrata et al. / Learning from data with structured missingness. In: Nature Machine Intelligence. 2023 ; Vol. 5, No. 1. pp. 13-23.

Bibtex

@article{1af594b889644d66a2dfcb6ccb840993,
title = "Learning from data with structured missingness",
abstract = "Missing data are an unavoidable complication in many machine learning tasks. When data are {\textquoteleft}missing at random{\textquoteright} 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 {\textquoteleft}structured missingness{\textquoteright} 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.",
keywords = "Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition, Human-Computer Interaction, Software",
author = "Robin Mitra and McGough, {Sarah F.} and Tapabrata Chakraborti and Chris Holmes and Ryan Copping and Niels Hagenbuch and Stefanie Biedermann and Jack Noonan and Brieuc Lehmann and Aditi Shenvi and Doan, {Xuan Vinh} and David Leslie and Ginestra Bianconi and Ruben Sanchez-Garcia and Alisha Davies and Maxine Mackintosh and Eleni-Rosalina Andrinopoulou and Anahid Basiri and Chris Harbron and MacArthur, {Ben D.}",
year = "2023",
month = jan,
day = "25",
doi = "10.1038/s42256-022-00596-z",
language = "English",
volume = "5",
pages = "13--23",
journal = "Nature Machine Intelligence",
issn = "2522-5839",
publisher = "Springer Science and Business Media LLC",
number = "1",

}

RIS

TY - JOUR

T1 - Learning from data with structured missingness

AU - Mitra, Robin

AU - McGough, Sarah F.

AU - Chakraborti, Tapabrata

AU - Holmes, Chris

AU - Copping, Ryan

AU - Hagenbuch, Niels

AU - Biedermann, Stefanie

AU - Noonan, Jack

AU - Lehmann, Brieuc

AU - Shenvi, Aditi

AU - Doan, Xuan Vinh

AU - Leslie, David

AU - Bianconi, Ginestra

AU - Sanchez-Garcia, Ruben

AU - Davies, Alisha

AU - Mackintosh, Maxine

AU - Andrinopoulou, Eleni-Rosalina

AU - Basiri, Anahid

AU - Harbron, Chris

AU - MacArthur, Ben D.

PY - 2023/1/25

Y1 - 2023/1/25

N2 - 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.

AB - 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.

KW - Artificial Intelligence

KW - Computer Networks and Communications

KW - Computer Vision and Pattern Recognition

KW - Human-Computer Interaction

KW - Software

U2 - 10.1038/s42256-022-00596-z

DO - 10.1038/s42256-022-00596-z

M3 - Journal article

VL - 5

SP - 13

EP - 23

JO - Nature Machine Intelligence

JF - Nature Machine Intelligence

SN - 2522-5839

IS - 1

ER -