On the Sample Complexity of Learning for Blind Inverse Problems

ArXi:2512.23405v4 Announce Type: replace Blind inverse problems arise in many experimental settings where both the signal of interest and the forward operator are (partially) unknown. In this context, methods developed for the non-blind case cannot be adapted in a straightforward manner due to identifiability issues and symmetric solutions inherent to the blind setting. Recently, data-driven approaches have been proposed to address such problems, nstrating strong empirical performance and adaptability.