Simone Brugiapaglia

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 38

Top Topics

High Dimensions

Compressed Sensing

Sparse Approximation

Efficient Algorithms For Computing

Top Venues

Comput. Math. Appl.

IEEE Trans. Inf. Theory

Publications

Marco Mignacca, Simone Brugiapaglia, Jason J. Bramburger
Real-Time Motion Detection Using Dynamic Mode Decomposition. CoRR (2024)
Aaron Berk, Simone Brugiapaglia, Tim Hoheisel
Square Root LASSO: Well-Posedness, Lipschitz Stability, and the Tuning Trade-Off. SIAM J. Optim. 34 (3) (2024)
Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks. CoRR (2024)
Nicola Rares Franco, Simone Brugiapaglia
A practical existence theorem for reduced order models based on convolutional autoencoders. CoRR (2024)
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 (2024)
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 (2024)
Giuseppe Alessio D'Inverno, Simone Brugiapaglia, Mirco Ravanelli
Generalization Limits of Graph Neural Networks in Identity Effects Learning. CoRR (2023)
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 (2023)
Aaron Berk, Simone Brugiapaglia, Tim Hoheisel
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing. SIAM J. Math. Data Sci. 5 (4) (2023)
Aaron Berk, Simone Brugiapaglia, Yaniv Plan, Matthew Scott, Xia Sheng, Özgür Yilmaz
Model-adapted Fourier sampling for generative compressed sensing. CoRR (2023)
Ben Adcock, Simone Brugiapaglia
Is Monte Carlo a bad sampling strategy for learning smooth functions in high dimensions? CoRR (2022)
Aaron Berk, Simone Brugiapaglia, Tim Hoheisel
LASSO reloaded: a variational analysis perspective with applications to compressed sensing. CoRR (2022)
Simone Brugiapaglia, Matthew Liu, Paul F. Tupper
Invariance, Encodings, and Generalization: Learning Identity Effects With Neural Networks. Neural Comput. 34 (8) (2022)
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) (2022)
Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks. CoRR (2022)
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) (2022)
Aaron Berk, Simone Brugiapaglia, Babhru Joshi, Yaniv Plan, Matthew Scott, Özgür Yilmaz
A coherence parameter characterizing generative compressed sensing with Fourier measurements. CoRR (2022)
Weiqi Wang, Simone Brugiapaglia
Compressive Fourier collocation methods for high-dimensional diffusion equations with periodic boundary conditions. CoRR (2022)
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 (2022)
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)
Simone Brugiapaglia, M. Liu, Paul F. Tupper
Invariance, encodings, and generalization: learning identity effects with neural networks. CoRR (2021)
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)
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 (2021)
Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
Iterative and greedy algorithms for the sparsity in levels model in compressed sensing. CoRR (2021)
Simone Brugiapaglia, Lorenzo Tamellini, Mattia Tani
Compressive isogeometric analysis. Comput. Math. Appl. 80 (12) (2020)
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 (2020)
Simone Brugiapaglia, Matthew Liu, Paul F. Tupper
Generalizing Outside the Training Set: When Can Neural Networks Learn Identity Effects? CoRR (2020)
Simone Brugiapaglia, Paul F. Tupper, Matthew Liu
Generalizing Outside the Training Set: When Can Neural Networks Learn Identity Effects? CogSci (2020)
Simone Brugiapaglia, Lorenzo Tamellini, Mattia Tani
Compressive Isogeometric Analysis. CoRR (2020)
Simone Brugiapaglia, Sjoerd Dirksen, Hans Christian Jung, Holger Rauhut
Sparse recovery in bounded Riesz systems with applications to numerical methods for PDEs. CoRR (2020)
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 (2020)
Ben Adcock, Anyi Bao, Simone Brugiapaglia
Correcting for unknown errors in sparse high-dimensional function approximation. Numerische Mathematik 142 (3) (2019)
Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing. CoRR (2019)
Ben Adcock, Claire Boyer, Simone Brugiapaglia
On oracle-type local recovery guarantees in compressed sensing. CoRR (2018)
Ben Adcock, Simone Brugiapaglia
Sparse approximation of multivariate functions from small datasets via weighted orthogonal matching pursuit. CoRR (2018)
Simone Brugiapaglia, Fabio Nobile, Stefano Micheletti, Simona Perotto
A theoretical study of COmpRessed SolvING for advection-diffusion-reaction problems. Math. Comput. 87 (309) (2018)
Simone Brugiapaglia, Ben Adcock
Robustness to Unknown Error in Sparse Regularization. IEEE Trans. Inf. Theory 64 (10) (2018)
Simone Brugiapaglia, Ben Adcock
Robustness to unknown error in sparse regularization. CoRR (2017)
Simone Brugiapaglia, Stefano Micheletti, Simona Perotto
Compressed solving: A numerical approximation technique for elliptic PDEs based on Compressed Sensing. Comput. Math. Appl. 70 (6) (2015)
Simone Brugiapaglia, Luca Gemignani
On the simultaneous refinement of the zeros of H-palindromic polynomials. J. Comput. Appl. Math. 272 (2014)