Derives LMMSE-based optimal estimators for blind inverse problems that are equivalent to tailored Tikhonov regularization and provides finite-sample error bounds explicitly depending on operator randomness.
IEEE Journal on Selected Areas in Information Theory1(1), 39–56 (2020)
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On the Sample Complexity of Learning for Blind Inverse Problems
Derives LMMSE-based optimal estimators for blind inverse problems that are equivalent to tailored Tikhonov regularization and provides finite-sample error bounds explicitly depending on operator randomness.