arXiv Machine Learning June 30, 2026 By Floor van Maarschalkerwaart, Subhadip Mukherjee, Christoph Brune, Marcello Carioni

A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems

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arXiv:2606. 30230v1 Announce Type: cross Abstract: Learned reconstruction operators for inverse problems are typically trained under a fixed noise model, and generalize poorly when the distribution during testing differs from the one assumed during training.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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