Learning Invariant Representations with Missing Data.
Ontology highlight
ABSTRACT:
SUBMITTER: Goldstein M
PROVIDER: S-EPMC10465015 | biostudies-literature | 2022 Apr
REPOSITORIES: biostudies-literature
Publications
Learning Invariant Representations with Missing Data.
Goldstein Mark M Puli Aahlad A Ranganath Rajesh R Jacobsen Jörn-Henrik JH Chau Olina O Saporta Adriel A Miller Andrew C AC
Proceedings of machine learning research 20220401
Spurious correlations allow flexible models to predict well during training but poorly on related test populations. Recent work has shown that models that satisfy particular independencies involving correlation-inducing <i>nuisance</i> variables have guarantees on their test performance. Enforcing such independencies requires nuisances to be observed during training. However, nuisances, such as demographics or image background labels, are often missing. Enforcing independence on just the observe ...[more]
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