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Simone Brugiapaglia
Simone Brugiapaglia
Assistant Professor, Concordia University, Department of Mathematics and Statistics
Verified email at concordia.ca - Homepage
Title
Cited by
Cited by
Year
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
63*2017
Sparse polynomial approximation of high-dimensional functions
B Adcock, S Brugiapaglia, CG Webster
SIAM, 2022
452022
Correcting for unknown errors in sparse high-dimensional function approximation
B Adcock, A Bao, S Brugiapaglia
Numerische Mathematik 142, 667-711, 2019
402019
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
352020
Robustness to unknown error in sparse regularization
S Brugiapaglia, B Adcock
IEEE Transactions on Information Theory 64 (10), 6638-6661, 2018
312018
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
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
242021
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
222015
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
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
152022
Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks
B Adcock, S Brugiapaglia, N Dexter, S Moraga
arXiv preprint arXiv:2211.12633, 2022
142022
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
112022
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
82022
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
72023
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
72020
Invariance, encodings, and generalization: learning identity effects with neural networks
S Brugiapaglia, M Liu, P Tupper
Neural Computation 34 (8), 1756-1789, 2022
6*2022
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
62021
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
62019
Sparse approximation of multivariate functions from small datasets via weighted orthogonal matching pursuit
B Adcock, S Brugiapaglia
Spectral and High Order Methods for Partial Differential Equations, 611, 2020
52020
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Articles 1–20