A meta-distribution-based robust optimization method learns RKHS uncertainty sets from relevant sources to guarantee out-of-distribution performance on unseen target distributions.
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Develops the first provably convergent stochastic fixed-point algorithm for free-support 2-Wasserstein barycenters of continuous measures under Caffarelli regularity, using a modified entropic OT map estimator.
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Provably convergent stochastic fixed-point algorithm for free-support Wasserstein barycenter of continuous non-parametric measures
Develops the first provably convergent stochastic fixed-point algorithm for free-support 2-Wasserstein barycenters of continuous measures under Caffarelli regularity, using a modified entropic OT map estimator.