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cs.LG2022
From Kernel Methods to Neural Networks: A Unifying Variational Formulation
Michael Unser
The minimization of a data-fidelity term and an additive regularization functional gives rise to a powerful framework for supervised learning. In this paper, we present a unifying…
cs.LG2021
Sparsest Univariate Learning Models Under Lipschitz Constraint
Shayan Aziznejad, Thomas Debarre, Michael Unser
Beside the minimization of the prediction error, two of the most desirable properties of a regression scheme are stability and interpretability. Driven by these principles, we prop…