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Simone Brugiapaglia
Simone Brugiapaglia
Associate Professor, Concordia University, Department of Mathematics and Statistics
Zweryfikowany adres z concordia.ca - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Compressed sensing approaches for polynomial approximation of high-dimensional functions
B Adcock, S Brugiapaglia, CG Webster
Compressed Sensing and its Applications: Second International MATHEON …, 2017
65*2017
Sparse polynomial approximation of high-dimensional functions
B Adcock, S Brugiapaglia, CG Webster
SIAM, 2022
532022
Correcting for unknown errors in sparse high-dimensional function approximation
B Adcock, A Bao, S Brugiapaglia
Numerische Mathematik 142, 667-711, 2019
442019
Deep neural networks are effective at learning high-dimensional Hilbert-valued functions from limited data
B Adcock, S Brugiapaglia, N Dexter, S Moraga
arXiv preprint arXiv:2012.06081, 2020
422020
Robustness to unknown error in sparse regularization
S Brugiapaglia, B Adcock
IEEE Transactions on Information Theory 64 (10), 6638-6661, 2018
332018
Sparse recovery in bounded Riesz systems with applications to numerical methods for PDEs
S Brugiapaglia, S Dirksen, HC Jung, H Rauhut
Applied and Computational Harmonic Analysis 53, 231-269, 2021
262021
A theoretical study of COmpRessed SolvING for advection-diffusion-reaction problems
S Brugiapaglia, F Nobile, S Micheletti, S Perotto
Mathematics of Computation 87 (309), 1-38, 2018
262018
Compressed solving: A numerical approximation technique for elliptic PDEs based on Compressed Sensing
S Brugiapaglia, S Micheletti, S Perotto
Computers & Mathematics with Applications 70 (6), 1306-1335, 2015
242015
Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks
B Adcock, S Brugiapaglia, N Dexter, S Moraga
Neural Networks, 106761, 2024
222024
Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing
B Adcock, S Brugiapaglia, M King–Roskamp
Foundations of Computational Mathematics 22 (1), 99-159, 2022
182022
On oracle-type local recovery guarantees in compressed sensing
B Adcock, C Boyer, S Brugiapaglia
Information and Inference: A Journal of the IMA 10 (1), 1-49, 2021
162021
LASSO reloaded: a variational analysis perspective with applications to compressed sensing
A Berk, S Brugiapaglia, T Hoheisel
SIAM Journal on Mathematics of Data Science 5 (4), 1102-1129, 2023
122023
COmpRessed SolvING: sparse approximation of PDEs based on compressed sensing
S Brugiapaglia
Politecnico di Milano, 2016
122016
On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples
B Adcock, S Brugiapaglia, N Dexter, S Moraga
arXiv preprint arXiv:2203.13908, 2022
102022
Invariance, encodings, and generalization: learning identity effects with neural networks
S Brugiapaglia, M Liu, P Tupper
Neural Computation 34 (8), 1756-1789, 2022
9*2022
A compressive spectral collocation method for the diffusion equation under the restricted isometry property
S Brugiapaglia
Quantification of Uncertainty: Improving Efficiency and Technology: QUIET …, 2020
82020
A coherence parameter characterizing generative compressed sensing with Fourier measurements
A Berk, S Brugiapaglia, B Joshi, Y Plan, M Scott, Ö Yilmaz
IEEE Journal on Selected Areas in Information Theory 3 (3), 502-512, 2022
72022
Wavelet–Fourier CORSING techniques for multidimensional advection–diffusion–reaction equations
S Brugiapaglia, S Micheletti, F Nobile, S Perotto
IMA Journal of Numerical Analysis 41 (4), 2744-2781, 2021
72021
Iterative and greedy algorithms for the sparsity in levels model in compressed sensing
B Adcock, S Brugiapaglia, M King-Roskamp
Wavelets and Sparsity XVIII 11138, 76-89, 2019
72019
Monte Carlo is a good sampling strategy for polynomial approximation in high dimensions
B Adcock, S Brugiapaglia
arXiv preprint arXiv:2208.09045, 2022
6*2022
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