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stat.ML2026
Anti-causal domain generalization: Leveraging unlabeled data
Sorawit Saengkyongam, Juan L. Gamella, Andrew C. Miller +3
The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments. Existing metho…
stat.ML2024
Considerations for Distribution Shift Robustness of Diagnostic Models in Healthcare
Arno Blaas, Adam GoliÅski, Andrew Miller +3
We consider robustness to distribution shifts in the context of diagnostic models in healthcare, where the prediction target , e.g., the presence of a disease, is causally upstr…