Probable Domain Generalization via Quantile Risk Minimization. (arXiv:2207.09944v2 [stat.ML] UPDATED)

Domain generalization (DG) seeks predictors which perform well on unseen test
distributions by leveraging data drawn from multiple related training
distributions or domains. To achieve this, DG is commonly formulated as an
average- or worst-case problem over the set of possible domains. However,
predictors that perform well on average lack robustness while predictors that
perform well in the worst case tend to be overly-conservative. To address this,
we propose a new probabilistic framework for DG where the goal is to …

arxiv quantile risk