Introduces structured DRO for learned inverse problem reconstructions with ambiguity sets aligned to the forward operator, yielding explicit dual representations and a worst-case bound that induces Tikhonov regularization on the operator Lipschitz constant.
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Reformulates Schrödinger problem on metric graphs via entropic OT with Γ-convergence to OT, derives dynamic version converging to Wasserstein distance and geodesics, and proves existence for general data.
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A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems
Introduces structured DRO for learned inverse problem reconstructions with ambiguity sets aligned to the forward operator, yielding explicit dual representations and a worst-case bound that induces Tikhonov regularization on the operator Lipschitz constant.
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The Schr\"odinger problem on metric graphs
Reformulates Schrödinger problem on metric graphs via entropic OT with Γ-convergence to OT, derives dynamic version converging to Wasserstein distance and geodesics, and proves existence for general data.