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Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems
In these cases, corrupted data or a surrogate, e. g. a simulator, is often used to produce training samples, meaning that there is a risk of obtaining misspecified priors.
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