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Learning from data with structured missingness

Published in arXiv (Cornell University) • Apr 4, 2023
NobleIDNI3P66W63R90S70
Authors:
Robin Mitra
,
Sarah F. McGough
,
Tapabrata Chakraborti

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 d...

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