C De Mol

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Mario BerteroProfessor Emeritus, University of GenovaVerified email at unige.it
Ingrid DaubechiesDuke UniversityVerified email at math.duke.edu
Patrizia BoccacciAssociate Professor of Computer Science, Genoa University ItalyVerified email at disi.unige.it
Domenico GiannoneInternational Monetary FundVerified email at imf.org
Lucrezia ReichlinProfessor of economics, London Business SchoolVerified email at london.edu
alessandro verriprofessor of computer science, dibris, università degli studi di genovaVerified email at unige.it
Francesca OdoneMaLGa Machine Learning Genoa Center - Università degli Studi di GenovaVerified email at unige.it
Ernesto De VitoMaLGa Machine Learning Genoa Center - Università di GenovaVerified email at unige.it
Lorenzo RosascoMaLGa Machine Learning Genoa Center - Università degli Studi di GenovaVerified email at unige.it
Riccardo ZanellaInjeniaVerified email at injenia.it
Luca ZanniProfessor of Numerical Analysis, University of Modena and Reggio Emilia, Modena, ItalyVerified email at unimore.it
Massimo RobbertoSpace Telescope Science InstituteVerified email at stsci.edu
Eric GautierToulouse School of EconomicsVerified email at tse-fr.eu
Jeffrey M. WooldridgeUniversity Distinguished Professor of Economics, Michigan State UniversityVerified email at msu.edu
Herman Koene van DijkErasmus University, Tinbergen InstituteVerified email at ese.eur.nl
Dominique LesselierLaboratoire des Signaux et Systèmes UMR8506 (CNRS-CentraleSupélec-Université Paris-Sud), Université Paris-SaclayVerified email at l2s.centralesupelec.fr

C De Mol

Université Libre de Bruxelles

Verified email at ulb.ac.be


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	5881	2004
Introduction to inverse problems in imaging M Bertero, P Boccacci, C De Mol CRC press, 2021	3189	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	619	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	608	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	384	1985
Elastic-net regularization in learning theory C De Mol, E De Vito, L Rosasco Journal of Complexity 25 (2), 201-230, 2009	346	2009
Linear inverse problems with discrete data: II. Stability and regularisation M Bertero, C De Mol, ER Pike Inverse problems 4 (3), 573, 1988	298	1988
The stability of inverse problems M Bertero, C De Mol, GA Viano Inverse scattering problems in optics, 161-214, 1980	162	1980
III Super-resolution by data inversion M Bertero, C De Mol Progress in optics 36, 129-178, 1996	107	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	103	1984
Optimal combination of survey forecasts C Conflitti, C De Mol, D Giannone International Journal of Forecasting 31 (4), 1096-1103, 2015	95	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	86	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	86	2009
An iterative algorithm for linear inverse problems with a sparsity constraint I Daubechies, M Defrise, C De Mol Commun. Pure Appl. Math 57, 1413-1457, 2004	82	2004
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	80	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	69	1983
An iterative thresholding algorithm for linear inverse problems with a sparsity contraint C DE MOL, I Daubechies, M DEFRISE Communications on pure and applied mathematics 57 (11), 1416-1457, 2004	66	2004
A note on wavelet-based inversion algorithms C De Mol, M Defrise Contemporary Mathematics 313, 85-96, 2002	66	2002
Inverse scattering problems in optics M Bertero, C De Mol, GA Viano Topics in current physics 20, 161-214, 1980	61	1980
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	54	1987

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