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.
and Baraniuk, Richard G
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
FMPlug adapts foundation flow-matching models into practical priors for inverse problems by combining instance-guided warm-start with sharp Gaussianity regularization, showing superior results on image restoration and scientific tasks with limited samples.
WaveGraphNet is a graph-based coupled inverse-forward model that localizes damage in CFRP plates from sparse guided-wave measurements with improved extrapolation to unseen locations.
citing papers explorer
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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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Saving Foundation Flow-Matching Priors for Inverse Problems
FMPlug adapts foundation flow-matching models into practical priors for inverse problems by combining instance-guided warm-start with sharp Gaussianity regularization, showing superior results on image restoration and scientific tasks with limited samples.
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WaveGraphNet: Physics-Consistent Guided-Wave Damage Localization through Coupled Inverse-Forward Graph Learning
WaveGraphNet is a graph-based coupled inverse-forward model that localizes damage in CFRP plates from sparse guided-wave measurements with improved extrapolation to unseen locations.