Adaptive optimization for prediction with missing data
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10994_2025_Article_6757.pdf
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2.55 MB
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970ce23c97065360342bb456ec41e382
Author(s)
Bertsimas, Dimitris
Delarue, Arthur
Pauphilet, Jean
Date Issued
March 24, 2025
Journal
Machine Learning
Publisher
Springer US
Citation
Bertsimas, D., Delarue, A. & Pauphilet, J. Adaptive optimization for prediction with missing data. Mach Learn 114, 124 (2025).
Version
Final published version
Abstract
When training predictive models on data with missing entries, the most widely used and versatile approach is a pipeline technique where we first impute missing entries and then compute predictions. In this paper, we view prediction with missing data as a two-stage adaptive optimization problem and propose a new class of models, adaptive linear regression models, where the regression coefficients adapt to the set of observed features. We show that some adaptive linear regression models are equivalent to learning an imputation rule and a downstream linear regression model simultaneously instead of sequentially. We leverage this joint-impute-then-regress interpretation to generalize our framework to non-linear models. In settings where data is strongly not missing at random, our methods achieve a 2–10% improvement in out-of-sample accuracy.
MIT Department
Sloan School of Management
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1007/s10994-025-06757-6
