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math.OC2026
DREAM: Deep-Reparametrization of Adaptive Regularization Maps for Fast Zero-Shot Self-Supervised Learning
Thanh Trung Vu, Ander Biguri, Christoph Kolbitsch +3
Adaptive regularization is an effective means of improving the flexibility of classical variational reconstruction methods while retaining their interpretability and mathematical s…
math.OC2025
Box-constrained L0 Bregman-relaxations
Mhamed Essafri, Luca Calatroni, Emmanuel Soubies
Regularization using the L0 pseudo-norm is a common approach to promote sparsity, with widespread applications in machine learning and signal processing. However, solving such prob…
math.OC2024
Exact continuous relaxations of l0-regularized criteria with non-quadratic data terms
M'hamed Essafri, Luca Calatroni, Emmanuel Soubies
We propose a new class of exact continuous relaxations of l0-regularized criteria involving non-quadratic data terms such as the Kullback-Leibler divergence and the logistic regres…