On the simultaneous refinement of the zeros of H-palindromic polynomials. - Simone Brugiapaglia; Luca Gemignani (J. Comput. Appl. Math., 2014)

Compressed solving: A numerical approximation technique for elliptic PDEs based on Compressed Sensing. - Simone Brugiapaglia; Stefano Micheletti; Simona Perotto (Comput. Math. Appl., 2015)

On oracle-type local recovery guarantees in compressed sensing. - Ben Adcock; Claire Boyer; Simone Brugiapaglia (CoRR, 2018)

Sparse approximation of multivariate functions from small datasets via weighted orthogonal matching pursuit. - Ben Adcock; Simone Brugiapaglia (CoRR, 2018)

A theoretical study of COmpRessed SolvING for advection-diffusion-reaction problems. - Simone Brugiapaglia; Fabio Nobile; Stefano Micheletti; Simona Perotto (Math. Comput., 2018)

Robustness to Unknown Error in Sparse Regularization. - Simone Brugiapaglia; Ben Adcock (IEEE Trans. Inf. Theory, 2018)

Correcting for unknown errors in sparse high-dimensional function approximation. - Ben Adcock; Anyi Bao; Simone Brugiapaglia (Numerische Mathematik, 2019)

Generalizing Outside the Training Set: When Can Neural Networks Learn Identity Effects? - Simone Brugiapaglia; Paul F. Tupper; Matthew Liu (CogSci, 2020)

Compressive isogeometric analysis. - Simone Brugiapaglia; Lorenzo Tamellini; Mattia Tani (Comput. Math. Appl., 2020)

Sparse recovery in bounded Riesz systems with applications to numerical methods for PDEs. - Simone Brugiapaglia; Sjoerd Dirksen; Hans Christian Jung; Holger Rauhut (CoRR, 2020)

Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data. - Ben Adcock; Simone Brugiapaglia; Nick C. Dexter; Sebastian Moraga (AAAI Spring Symposium: MLPS, 2021)

Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. - Ben Adcock; Simone Brugiapaglia; Nick C. Dexter; Sebastian Moraga (MSML, 2021)

Invariance, encodings, and generalization: learning identity effects with neural networks. - Simone Brugiapaglia; M. Liu; Paul F. Tupper (CoRR, 2021)

Iterative and greedy algorithms for the sparsity in levels model in compressed sensing. - Ben Adcock; Simone Brugiapaglia; Matthew King-Roskamp (CoRR, 2021)

The Benefits of Acting Locally: Reconstruction Algorithms for Sparse in Levels Signals With Stable and Robust Recovery Guarantees. - Ben Adcock; Simone Brugiapaglia; Matthew King-Roskamp (IEEE Trans. Signal Process., 2021)

On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples. - Ben Adcock; Simone Brugiapaglia; Nick C. Dexter; Sebastian Moraga (CoRR, 2022)

Compressive Fourier collocation methods for high-dimensional diffusion equations with periodic boundary conditions. - Weiqi Wang; Simone Brugiapaglia (CoRR, 2022)

Is Monte Carlo a bad sampling strategy for learning smooth functions in high dimensions? - Ben Adcock; Simone Brugiapaglia (CoRR, 2022)

Do Log Factors Matter? On Optimal Wavelet Approximation and the Foundations of Compressed Sensing. - Ben Adcock; Simone Brugiapaglia; Matthew King-Roskamp (Found. Comput. Math., 2022)

Invariance, Encodings, and Generalization: Learning Identity Effects With Neural Networks. - Simone Brugiapaglia; Matthew Liu; Paul F. Tupper (Neural Comput., 2022)

A Coherence Parameter Characterizing Generative Compressed Sensing With Fourier Measurements. - Aaron Berk; Simone Brugiapaglia; Babhru Joshi; Yaniv Plan; Matthew Scott 003; Özgür Yilmaz (IEEE J. Sel. Areas Inf. Theory, 2022)

The greedy side of the LASSO: New algorithms for weighted sparse recovery via loss function-based orthogonal matching pursuit. - Sina Mohammad-Taheri; Simone Brugiapaglia (CoRR, 2023)

Model-adapted Fourier sampling for generative compressed sensing. - Aaron Berk; Simone Brugiapaglia; Yaniv Plan; Matthew Scott 003; Xia Sheng; Özgür Yilmaz (CoRR, 2023)

LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing. - Aaron Berk; Simone Brugiapaglia; Tim Hoheisel (SIAM J. Math. Data Sci., 2023)

A practical existence theorem for reduced order models based on convolutional autoencoders. - Nicola Rares Franco; Simone Brugiapaglia (CoRR, 2024)

Neural Rank Collapse: Weight Decay and Small Within-Class Variability Yield Low-Rank Bias. - Emanuele Zangrando; Piero Deidda; Simone Brugiapaglia; Nicola Guglielmi; Francesco Tudisco (CoRR, 2024)

Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks. - Ben Adcock; Simone Brugiapaglia; Nick C. Dexter; Sebastian Moraga (CoRR, 2024)

Physics-informed deep learning and compressive collocation for high-dimensional diffusion-reaction equations: practical existence theory and numerics. - Simone Brugiapaglia; Nick C. Dexter; Samir Karam; Weiqi Wang (CoRR, 2024)

Square Root LASSO: Well-Posedness, Lipschitz Stability, and the Tuning Trade-Off. - Aaron Berk; Simone Brugiapaglia; Tim Hoheisel (SIAM J. Optim., 2024)

Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks. - Ben Adcock; Simone Brugiapaglia; Nick C. Dexter; Sebastian Moraga (Neural Networks, 2025)

Generalization limits of Graph Neural Networks in identity effects learning. - Giuseppe Alessio D'Inverno; Simone Brugiapaglia; Mirco Ravanelli (Neural Networks, 2025)

Surrogate models for diffusion on graphs via sparse polynomials. - Giuseppe Alessio D'Inverno; Kylian Ajavon; Simone Brugiapaglia (CoRR, 2025)

Reconstruction of frequency-localized functions from pointwise samples via least squares and deep learning. - A. Martina Neuman; Andres Felipe Lerma Pineda; Jason J. Bramburger; Simone Brugiapaglia (CoRR, 2025)

Deep greedy unfolding: Sorting out argsorting in greedy sparse recovery algorithms. - Sina Mohammad-Taheri; Matthew J. Colbrook; Simone Brugiapaglia (CoRR, 2025)

Fast One-Pass Sparse Approximation of the Top Eigenvectors of Huge Low-Rank Matrices? Yes, MAM*! - Edem Boahen; Simone Brugiapaglia; Hung-Hsu Chou; Mark A. Iwen; Felix Krahmer (CoRR, 2025)

Real-time motion detection using dynamic mode decomposition. - Marco Mignacca; Simone Brugiapaglia; Jason J. Bramburger (EURASIP J. Image Video Process., 2025)