| 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 | 67* | 2017 |
| Sparse polynomial approximation of high-dimensional functions B Adcock, S Brugiapaglia, CG Webster SIAM, 2022 | 65 | 2022 |
| Correcting for unknown errors in sparse high-dimensional function approximation B Adcock, A Bao, S Brugiapaglia Numerische Mathematik 142 (3), 667-711, 2019 | 49 | 2019 |
| Deep neural networks are effective at learning high-dimensional Hilbert-valued functions from limited data B Adcock, S Brugiapaglia, N Dexter, S Moraga Proceedings of Machine Learning Research 145, 1-36, 2022 | 46 | 2022 |
| Robustness to unknown error in sparse regularization S Brugiapaglia, B Adcock IEEE Transactions on Information Theory 64 (10), 6638-6661, 2018 | 33 | 2018 |
| 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 | 31 | 2021 |
| Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks B Adcock, S Brugiapaglia, N Dexter, S Moraga Neural Networks 181, 106761, 2025 | 27 | 2025 |
| 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 | 26 | 2018 |
| 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 | 25 | 2015 |
| 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 | 20 | 2022 |
| 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 | 17 | 2021 |
| 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 Memoirs of the European Mathematical Society 13, 2024 | 16 | 2024 |
| 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 | 15 | 2023 |
| COmpRessed SolvING: sparse approximation of PDEs based on compressed sensing S Brugiapaglia Politecnico di Milano, 2016 | 12 | 2016 |
| Monte Carlo is a good sampling strategy for polynomial approximation in high dimensions B Adcock, S Brugiapaglia arXiv preprint arXiv:2208.09045, 2022 | 10* | 2022 |
| Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks B Adcock, S Brugiapaglia, N Dexter, S Moraga arXiv preprint arXiv:2404.03761, 2024 | 9 | 2024 |
| 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 | 9 | 2022 |
| 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 | 8 | 2020 |
| 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 | 7 | 2021 |
Ben AdcockProfessor, Simon Fraser Universitysfu.ca의 이메일 확인됨
