A meta-distribution-based robust optimization method learns RKHS uncertainty sets from relevant sources to guarantee out-of-distribution performance on unseen target distributions.
On the equivalence between kernel quadrature rules and random feature expan- sions.Journal of Machine Learning Research, 18(21):1–38, 2017
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Robust Out-of-Distribution Stochastic Optimization
A meta-distribution-based robust optimization method learns RKHS uncertainty sets from relevant sources to guarantee out-of-distribution performance on unseen target distributions.