Considerations for Distribution Shift Robustness in Health

      *=Equal Contributors
This paper was accepted at the workshop “Trustworthy Machine Learning for Healthcare Workshop” at the conference ICLR 2023.
When analyzing robustness of predictive models under distribution shift, many works focus on tackling generalization in the presence of spurious correlations. In this case, one typically makes use of covariates or environment indicators to enforce independencies in learned models to guarantee generalization under various distribution shifts. In this work, we analyze a class of distribution shifts, where such independencies are not desirable, as… Read More Apple Machine Learning Research 







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