Simone Brugiapaglia

50 papers B 1Journal 47Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A short tour of operator learning theory: Convergence rates, statistical limits, and open questions. CoRR Simone Brugiapaglia, Nicola Rares Franco, Nicholas H. Nelsen
2026	J	`jnl`	Approximating Matrix Functions with Deep Neural Networks and Transformers. CoRR Rahul Padmanabhan, Simone Brugiapaglia
2025	J	`jnl`	Deep greedy unfolding: Sorting out argsorting in greedy sparse recovery algorithms. CoRR Sina Mohammad-Taheri, Matthew J. Colbrook, Simone Brugiapaglia
2025	J	`jnl`	Fast One-Pass Sparse Approximation of the Top Eigenvectors of Huge Low-Rank Matrices? Yes, MAM CoRR Edem Boahen, Simone Brugiapaglia, Hung-Hsu Chou, Mark A. Iwen, Felix Krahmer
2025	J	`jnl`	Generalization limits of Graph Neural Networks in identity effects learning. Neural Networks Giuseppe Alessio D'Inverno, Simone Brugiapaglia, Mirco Ravanelli
2025	J	`jnl`	Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks. Neural Networks Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
2025	J	`jnl`	Physics-Informed Deep Learning and Compressive Collocation for High-Dimensional Diffusion-Reaction Equations: Practical Existence Theory and Numerics. J. Mach. Learn. Res. Simone Brugiapaglia, Nick C. Dexter, Samir Karam, Weiqi Wang
2025	J	`jnl`	Real-time motion detection using dynamic mode decomposition. EURASIP J. Image Video Process. Marco Mignacca, Simone Brugiapaglia, Jason J. Bramburger
2025	J	`jnl`	Reconstruction of frequency-localized functions from pointwise samples via least squares and deep learning. CoRR A. Martina Neuman, Andres Felipe Lerma Pineda, Jason J. Bramburger, Simone Brugiapaglia
2025	J	`jnl`	Surrogate models for diffusion on graphs via sparse polynomials. CoRR Giuseppe Alessio D'Inverno, Kylian Ajavon, Simone Brugiapaglia
2024	J	`jnl`	A practical existence theorem for reduced order models based on convolutional autoencoders. CoRR Nicola Rares Franco, Simone Brugiapaglia
2024	J	`jnl`	Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks. CoRR Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
2024	J	`jnl`	Neural Rank Collapse: Weight Decay and Small Within-Class Variability Yield Low-Rank Bias. CoRR Emanuele Zangrando, Piero Deidda, Simone Brugiapaglia, Nicola Guglielmi, Francesco Tudisco
2024	J	`jnl`	Physics-informed deep learning and compressive collocation for high-dimensional diffusion-reaction equations: practical existence theory and numerics. CoRR Simone Brugiapaglia, Nick C. Dexter, Samir Karam, Weiqi Wang
2024	J	`jnl`	Real-Time Motion Detection Using Dynamic Mode Decomposition. CoRR Marco Mignacca, Simone Brugiapaglia, Jason J. Bramburger
2024	J	`jnl`	Square Root LASSO: Well-Posedness, Lipschitz Stability, and the Tuning Trade-Off. SIAM J. Optim. Aaron Berk, Simone Brugiapaglia, Tim Hoheisel
2023	J	`jnl`	Generalization Limits of Graph Neural Networks in Identity Effects Learning. CoRR Giuseppe Alessio D'Inverno, Simone Brugiapaglia, Mirco Ravanelli
2023	J	`jnl`	LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing. SIAM J. Math. Data Sci. Aaron Berk, Simone Brugiapaglia, Tim Hoheisel
2023	J	`jnl`	Model-adapted Fourier sampling for generative compressed sensing. CoRR Aaron Berk, Simone Brugiapaglia, Yaniv Plan, Matthew Scott, Xia Sheng, Özgür Yilmaz
2023	J	`jnl`	The greedy side of the LASSO: New algorithms for weighted sparse recovery via loss function-based orthogonal matching pursuit. CoRR Sina Mohammad-Taheri, Simone Brugiapaglia
2022	J	`jnl`	A Coherence Parameter Characterizing Generative Compressed Sensing With Fourier Measurements. IEEE J. Sel. Areas Inf. Theory Aaron Berk, Simone Brugiapaglia, Babhru Joshi, Yaniv Plan, Matthew Scott, Özgür Yilmaz
2022	J	`jnl`	A coherence parameter characterizing generative compressed sensing with Fourier measurements. CoRR Aaron Berk, Simone Brugiapaglia, Babhru Joshi, Yaniv Plan, Matthew Scott, Özgür Yilmaz
2022	J	`jnl`	Compressive Fourier collocation methods for high-dimensional diffusion equations with periodic boundary conditions. CoRR Weiqi Wang, Simone Brugiapaglia
2022	J	`jnl`	Do Log Factors Matter? On Optimal Wavelet Approximation and the Foundations of Compressed Sensing. Found. Comput. Math. Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
2022	J	`jnl`	Invariance, Encodings, and Generalization: Learning Identity Effects With Neural Networks. Neural Comput. Simone Brugiapaglia, Matthew Liu, Paul F. Tupper
2022	J	`jnl`	Is Monte Carlo a bad sampling strategy for learning smooth functions in high dimensions? CoRR Ben Adcock, Simone Brugiapaglia
2022	J	`jnl`	LASSO reloaded: a variational analysis perspective with applications to compressed sensing. CoRR Aaron Berk, Simone Brugiapaglia, Tim Hoheisel
2022	J	`jnl`	Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks. CoRR Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
2022	J	`jnl`	On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples. CoRR Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
2021	—	`conf`	Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. MSML Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
2021	J	`jnl`	Invariance, encodings, and generalization: learning identity effects with neural networks. CoRR Simone Brugiapaglia, M. Liu, Paul F. Tupper
2021	J	`jnl`	Iterative and greedy algorithms for the sparsity in levels model in compressed sensing. CoRR Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
2021	—	`conf`	Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data. AAAI Spring Symposium: MLPS Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
2021	J	`jnl`	The Benefits of Acting Locally: Reconstruction Algorithms for Sparse in Levels Signals With Stable and Robust Recovery Guarantees. IEEE Trans. Signal Process. Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
2020	J	`jnl`	Compressive Isogeometric Analysis. CoRR Simone Brugiapaglia, Lorenzo Tamellini, Mattia Tani
2020	J	`jnl`	Compressive isogeometric analysis. Comput. Math. Appl. Simone Brugiapaglia, Lorenzo Tamellini, Mattia Tani
2020	J	`jnl`	Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. CoRR Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
2020	B	`conf`	Generalizing Outside the Training Set: When Can Neural Networks Learn Identity Effects? CogSci Simone Brugiapaglia, Paul F. Tupper, Matthew Liu
2020	J	`jnl`	Generalizing Outside the Training Set: When Can Neural Networks Learn Identity Effects? CoRR Simone Brugiapaglia, Matthew Liu, Paul F. Tupper
2020	J	`jnl`	Sparse recovery in bounded Riesz systems with applications to numerical methods for PDEs. CoRR Simone Brugiapaglia, Sjoerd Dirksen, Hans Christian Jung, Holger Rauhut
2020	J	`jnl`	The benefits of acting locally: Reconstruction algorithms for sparse in levels signals with stable and robust recovery guarantees. CoRR Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
2019	J	`jnl`	Correcting for unknown errors in sparse high-dimensional function approximation. Numerische Mathematik Ben Adcock, Anyi Bao, Simone Brugiapaglia
2019	J	`jnl`	Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing. CoRR Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
2018	J	`jnl`	A theoretical study of COmpRessed SolvING for advection-diffusion-reaction problems. Math. Comput. Simone Brugiapaglia, Fabio Nobile, Stefano Micheletti, Simona Perotto
2018	J	`jnl`	On oracle-type local recovery guarantees in compressed sensing. CoRR Ben Adcock, Claire Boyer, Simone Brugiapaglia
2018	J	`jnl`	Robustness to Unknown Error in Sparse Regularization. IEEE Trans. Inf. Theory Simone Brugiapaglia, Ben Adcock
2018	J	`jnl`	Sparse approximation of multivariate functions from small datasets via weighted orthogonal matching pursuit. CoRR Ben Adcock, Simone Brugiapaglia
2017	J	`jnl`	Robustness to unknown error in sparse regularization. CoRR Simone Brugiapaglia, Ben Adcock
2015	J	`jnl`	Compressed solving: A numerical approximation technique for elliptic PDEs based on Compressed Sensing. Comput. Math. Appl. Simone Brugiapaglia, Stefano Micheletti, Simona Perotto
2014	J	`jnl`	On the simultaneous refinement of the zeros of H-palindromic polynomials. J. Comput. Appl. Math. Simone Brugiapaglia, Luca Gemignani