Speaker
Peter Bühlmann
(ETH Zurich, Seminar for Statistics)
Description
Statistical models and machine learning algorithms are often deployed in populations that differ from those on which they were trained, a challenge that is particularly acute in digital health. We discuss domain generalization and adaptation for a large-scale database from multiple countries with intensive care unit (ICU) data. We introduce Distributionally Robust Invariance Learning as an approach to exploiting stable structure across environments, and conclude with a brief discussion of the potential and limitations of a novel foundation model in this context.
| Classification | Mainly methodology |
|---|---|
| Keywords | machine learning |
Primary author
Peter Bühlmann
(ETH Zurich, Seminar for Statistics)