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papersSEP 10 04:00 UTC

Bayes-Optimal Diagonal Regularization in Modal Inverse Problems Follows Closed-Form Power Law

A new machine learning theory paper establishes a 'diagonal saturation principle' for modal inverse problems. When truncation noise is isotropic, the optimal diagonal Tikhonov regularizer takes a closed-form power-law shape whose exponent is fixed entirely by the prior. The authors argue this explains why learned regularization converges on an analytic solution rather than a data-dependent one.

Bayesian inferenceTikhonov regularizationinverse problemsmachine-learning-theorypower lawregularization

COVERAGE · 1 REPORT · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGWhy Learning Rediscovers the Closed-Form Diagonal Regularizer ↗SEP 10 04:00 UTC

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