| 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, 2004 | 4213 | 2004 |
| 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 | 432 | 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 | 371 | 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 | 346 | 1985 |
| Linear inverse problems with discrete data: II. Stability and regularisation M Bertero, C De Mol, ER Pike Inverse problems 4 (3), 573, 1988 | 267 | 1988 |
| Elastic-net regularization in learning theory C De Mol, E De Vito, L Rosasco Journal of Complexity 25 (2), 201-230, 2009 | 193 | 2009 |
| The stability of inverse problems M Bertero, C De Mol, GA Viano Inverse scattering problems in optics, 161-214, 1980 | 133 | 1980 |
| III Super-resolution by data inversion M Bertero, C De Mol Progress in optics 36, 129-178, 1996 | 92 | 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 | 91 | 1984 |
| 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 | 75 | 2009 |
| 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 | 68 | 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 | 67 | 1983 |
| Feature selection for high-dimensional data A Destrero, S Mosci, C De Mol, A Verri, F Odone Computational management science 6 (1), 25-40, 2009 | 64 | 2009 |
| Resolution in diffraction-limited imaging, a singular value analysis: IV. The case of uncertain localization or non-uniform illumination of the object M Bertero, C De Mol, ER Pike, JG Walker Optica acta 31 (8), 923-946, 1984 | 64 | 1984 |
| A note on wavelet-based inversion algorithms C De Mol, M Defrise Contemporary Mathematics 313, 85-96, 2002 | 59 | 2002 |
| Optimal combination of survey forecasts C Conflitti, C De Mol, D Giannone International Journal of Forecasting 31 (4), 1096-1103, 2015 | 46 | 2015 |
| 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 | 42 | 1983 |
| Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components? C De Mol, D Giannone, L Reichlin ECB working paper, 2006 | 41 | 2006 |
| 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 | 39 | 2008 |
| Regularized iterative and noniterative procedures for object restoration in the presence of noise: an error analysis JB Abbiss, M Defrise, C De Mol, HS Dhadwal JOSA 73 (11), 1470-1475, 1983 | 39 | 1983 |
Mario BerteroProfessor in Information Science, University of GenovaVerified email at unige.it
