Mitra, Robin ![]() |
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: | Article |
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Date Type: | Publication |
Status: | Published |
Schools: | Mathematics |
Publisher: | Nature Research |
ISSN: | 2522-5839 |
Date of Acceptance: | 21 November 2022 |
Last Modified: | 06 May 2023 02:01 |
URI: | https://orca.cardiff.ac.uk/id/eprint/158506 |
Citation Data
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