C De Mol
C De Mol
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An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
I Daubechies, M Defrise, C De Mol
Communications on Pure and Applied Mathematics 57 (11), 1413-1457, 2004
Introduction to inverse problems in imaging
M Bertero, P Boccacci, C De Mol
CRC press, 2021
Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?
C De Mol, D Giannone, L Reichlin
Journal of Econometrics 146 (2), 318-328, 2008
Sparse and stable Markowitz portfolios
J Brodie, I Daubechies, C De Mol, D Giannone, I Loris
Proceedings of the National Academy of Sciences 106 (30), 12267-12272, 2009
Linear inverse problems with discrete data. I. General formulation and singular system analysis
M Bertero, C De Mol, ER Pike
Inverse problems 1 (4), 301, 1985
Elastic-net regularization in learning theory
C De Mol, E De Vito, L Rosasco
Journal of Complexity 25 (2), 201-230, 2009
Linear inverse problems with discrete data: II. Stability and regularisation
M Bertero, C De Mol, ER Pike
Inverse problems 4 (3), 573, 1988
The stability of inverse problems
M Bertero, C De Mol, GA Viano
Inverse scattering problems in optics, 161-214, 1980
III Super-resolution by data inversion
M Bertero, C De Mol
Progress in optics 36, 129-178, 1996
Generalised information theory for inverse problems in signal processing
ER Pike, JG McWhirter, M Bertero, C De Mol
IEE Proceedings F (Communications, Radar and Signal Processing) 131 (6), 660-667, 1984
Optimal combination of survey forecasts
C Conflitti, C De Mol, D Giannone
International Journal of Forecasting 31 (4), 1096-1103, 2015
A regularized method for selecting nested groups of relevant genes from microarray data
C De Mol, S Mosci, M Traskine, A Verri
Journal of Computational Biology 16 (5), 677-690, 2009
Feature selection for high-dimensional data
A Destrero, S Mosci, C De Mol, A Verri, F Odone
Computational management science 6, 25-40, 2009
Accelerating gradient projection methods for ℓ1-constrained signal recovery by steplength selection rules
I Loris, M Bertero, C De Mol, R Zanella, L Zanni
Applied and Computational Harmonic Analysis 27 (2), 247-254, 2009
Regularized iterative and non-iterative procedures for object restoration from experimental data
JB Abbiss, C De Mol, HS Dhadwal
Optica Acta: International Journal of Optics 30 (1), 107-124, 1983
A note on wavelet-based inversion algorithms
C De Mol, M Defrise
Contemporary Mathematics 313, 85-96, 2002
A note on stopping rules for iterative regularisation methods and filtered SVD
M Defrise, C De Mol
Inverse Problems: An Interdisciplinary Study, 261-268, 1987
A regularized iterative algorithm for limited-angle inverse Radon transform
M Defrise, C De Mol
Optica Acta: International Journal of Optics 30 (4), 403-408, 1983
Inverse scattering problems in optics
M Bertero, C De Mol, GA Viano
Topics in current physics 20, 161, 1980
A sparsity-enforcing method for learning face features
A Destrero, C De Mol, F Odone, A Verri
IEEE Transactions on Image Processing 18 (1), 188-201, 2008
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