Explicitly modeling and marginalizing environment variation via generalized random-intercept models produces representations that support robust average prediction across unseen environments and outperform invariant-learning methods in challenging distribution-shift settings.
Learning models with uniform performance via distributionally robust optimization
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Robust Representation Learning through Explicit Environment Modeling
Explicitly modeling and marginalizing environment variation via generalized random-intercept models produces representations that support robust average prediction across unseen environments and outperform invariant-learning methods in challenging distribution-shift settings.