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Journal Articles
- 2024
[j12]Aaron Berk, Simone Brugiapaglia
, Tim Hoheisel:
Square Root LASSO: Well-Posedness, Lipschitz Stability, and the Tuning Trade-Off. SIAM J. Optim. 34(3): 2609-2637 (2024)- 2023
- [j11]Aaron Berk, Simone Brugiapaglia, Tim Hoheisel:
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing. SIAM J. Math. Data Sci. 5(4): 1102-1129 (2023) - 2022
- [j10]Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp:
Do Log Factors Matter? On Optimal Wavelet Approximation and the Foundations of Compressed Sensing. Found. Comput. Math. 22(1): 99-159 (2022) - [j9]Aaron Berk, Simone Brugiapaglia, Babhru Joshi, Yaniv Plan, Matthew Scott, Özgür Yilmaz:
A Coherence Parameter Characterizing Generative Compressed Sensing With Fourier Measurements. IEEE J. Sel. Areas Inf. Theory 3(3): 502-512 (2022) - [j8]Simone Brugiapaglia, Matthew Liu, Paul F. Tupper:
Invariance, Encodings, and Generalization: Learning Identity Effects With Neural Networks. Neural Comput. 34(8): 1756-1789 (2022) - 2021
- [j7]Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp:
The Benefits of Acting Locally: Reconstruction Algorithms for Sparse in Levels Signals With Stable and Robust Recovery Guarantees. IEEE Trans. Signal Process. 69: 3160-3175 (2021) - 2020
- [j6]Simone Brugiapaglia, Lorenzo Tamellini, Mattia Tani:
Compressive isogeometric analysis. Comput. Math. Appl. 80(12): 3137-3155 (2020) - 2019
- [j5]Ben Adcock, Anyi Bao, Simone Brugiapaglia:
Correcting for unknown errors in sparse high-dimensional function approximation. Numerische Mathematik 142(3): 667-711 (2019) - 2018
- [j4]Simone Brugiapaglia, Fabio Nobile, Stefano Micheletti, Simona Perotto:
A theoretical study of COmpRessed SolvING for advection-diffusion-reaction problems. Math. Comput. 87(309): 1-38 (2018) - [j3]Simone Brugiapaglia, Ben Adcock:
Robustness to Unknown Error in Sparse Regularization. IEEE Trans. Inf. Theory 64(10): 6638-6661 (2018) - 2015
- [j2]Simone Brugiapaglia, Stefano Micheletti, Simona Perotto:
Compressed solving: A numerical approximation technique for elliptic PDEs based on Compressed Sensing. Comput. Math. Appl. 70(6): 1306-1335 (2015) - 2014
- [j1]Simone Brugiapaglia, Luca Gemignani:
On the simultaneous refinement of the zeros of H-palindromic polynomials. J. Comput. Appl. Math. 272: 293-303 (2014)
Conference and Workshop Papers
- 2021
- [c3]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data. AAAI Spring Symposium: MLPS 2021 - [c2]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. MSML 2021: 1-36 - 2020
- [c1]Simone Brugiapaglia, Paul F. Tupper, Matthew Liu:
Generalizing Outside the Training Set: When Can Neural Networks Learn Identity Effects? CogSci 2020
Informal and Other Publications
- 2024
- [i25]Nicola Rares Franco, Simone Brugiapaglia:
A practical existence theorem for reduced order models based on convolutional autoencoders. CoRR abs/2402.00435 (2024) - [i24]Emanuele Zangrando, Piero Deidda, Simone Brugiapaglia, Nicola Guglielmi, Francesco Tudisco:
Neural Rank Collapse: Weight Decay and Small Within-Class Variability Yield Low-Rank Bias. CoRR abs/2402.03991 (2024) - [i23]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks. CoRR abs/2404.03761 (2024) - [i22]Marco Mignacca, Simone Brugiapaglia, Jason J. Bramburger:
Real-Time Motion Detection Using Dynamic Mode Decomposition. CoRR abs/2405.05057 (2024) - [i21]Simone Brugiapaglia, Nick C. Dexter, Samir Karam, Weiqi Wang:
Physics-informed deep learning and compressive collocation for high-dimensional diffusion-reaction equations: practical existence theory and numerics. CoRR abs/2406.01539 (2024) - 2023
- [i20]Sina Mohammad-Taheri, Simone Brugiapaglia:
The greedy side of the LASSO: New algorithms for weighted sparse recovery via loss function-based orthogonal matching pursuit. CoRR abs/2303.00844 (2023) - [i19]Giuseppe Alessio D'Inverno, Simone Brugiapaglia, Mirco Ravanelli:
Generalization Limits of Graph Neural Networks in Identity Effects Learning. CoRR abs/2307.00134 (2023) - [i18]Aaron Berk, Simone Brugiapaglia, Yaniv Plan, Matthew Scott, Xia Sheng, Özgür Yilmaz:
Model-adapted Fourier sampling for generative compressed sensing. CoRR abs/2310.04984 (2023) - 2022
- [i17]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples. CoRR abs/2203.13908 (2022) - [i16]Aaron Berk, Simone Brugiapaglia, Tim Hoheisel:
LASSO reloaded: a variational analysis perspective with applications to compressed sensing. CoRR abs/2205.06872 (2022) - [i15]Weiqi Wang, Simone Brugiapaglia:
Compressive Fourier collocation methods for high-dimensional diffusion equations with periodic boundary conditions. CoRR abs/2206.01255 (2022) - [i14]Aaron Berk, Simone Brugiapaglia, Babhru Joshi, Yaniv Plan, Matthew Scott, Özgür Yilmaz:
A coherence parameter characterizing generative compressed sensing with Fourier measurements. CoRR abs/2207.09340 (2022) - [i13]Ben Adcock, Simone Brugiapaglia:
Is Monte Carlo a bad sampling strategy for learning smooth functions in high dimensions? CoRR abs/2208.09045 (2022) - [i12]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks. CoRR abs/2211.12633 (2022) - 2021
- [i11]Simone Brugiapaglia, M. Liu, Paul F. Tupper:
Invariance, encodings, and generalization: learning identity effects with neural networks. CoRR abs/2101.08386 (2021) - [i10]Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp:
Iterative and greedy algorithms for the sparsity in levels model in compressed sensing. CoRR abs/2110.15420 (2021) - 2020
- [i9]Simone Brugiapaglia, Lorenzo Tamellini, Mattia Tani:
Compressive Isogeometric Analysis. CoRR abs/2003.06475 (2020) - [i8]Simone Brugiapaglia, Matthew Liu, Paul F. Tupper:
Generalizing Outside the Training Set: When Can Neural Networks Learn Identity Effects? CoRR abs/2005.04330 (2020) - [i7]Simone Brugiapaglia, Sjoerd Dirksen, Hans Christian Jung, Holger Rauhut:
Sparse recovery in bounded Riesz systems with applications to numerical methods for PDEs. CoRR abs/2005.06994 (2020) - [i6]Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp:
The benefits of acting locally: Reconstruction algorithms for sparse in levels signals with stable and robust recovery guarantees. CoRR abs/2006.13389 (2020) - [i5]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. CoRR abs/2012.06081 (2020) - 2019
- [i4]Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp:
Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing. CoRR abs/1905.10028 (2019) - 2018
- [i3]Ben Adcock, Claire Boyer, Simone Brugiapaglia:
On oracle-type local recovery guarantees in compressed sensing. CoRR abs/1806.03789 (2018) - [i2]Ben Adcock, Simone Brugiapaglia:
Sparse approximation of multivariate functions from small datasets via weighted orthogonal matching pursuit. CoRR abs/1810.11115 (2018) - 2017
- [i1]Simone Brugiapaglia, Ben Adcock:
Robustness to unknown error in sparse regularization. CoRR abs/1705.10299 (2017)
Coauthor Index
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