3 papers
cs.LG2026
On the Sample Complexity of Learning for Blind Inverse Problems
Nathan Buskulic, Luca Calatroni, Lorenzo Rosasco +1
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 fo…
cs.IT2026
A Comparative Study of MAP and LMMSE Estimators for Blind Inverse Problems
Nathan Buskulic, Luca Calatroni
Maximum-a-posteriori (MAP) approaches are an effective framework for inverse problems with known forward operators, particularly when combined with expressive priors and careful pa…
math.OC2025
Deep Equilibrium models for Poisson Imaging Inverse problems via Mirror Descent
Christian Daniele, Silvia Villa, Samuel Vaiter +1
Deep Equilibrium Models (DEQs) are implicit neural networks with fixed points, which have recently gained attention for learning image regularization functionals, particularly in s…