3 papers
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…
cs.LG2025
Wearable Accelerometer Foundation Models for Health via Knowledge Distillation
Salar Abbaspourazad, Anshuman Mishra, Joseph Futoma +2
Modern wearable devices can conveniently record various biosignals in the many different environments of daily living, enabling a rich view of individual health. However, not all b…
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…