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Relax and penalize: a new bilevel approach to mixed-binary hyperparameter optimization
In recent years, bilevel approaches have become very popular to efficiently estimate high-dimensional hyperparameters of machine learning models.
Bilevel learning of the Group Lasso structure
Regression with group-sparsity penalty plays a central role in high-dimensional prediction problems.
Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising
Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures.
On-the-fly Approximation of Multivariate Total Variation Minimization
In the context of change-point detection, addressed by Total Variation minimization strategies, an efficient on-the-fly algorithm has been designed leading to exact solutions for univariate data.
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