Parameter instability regimes for sparse proximal denoising programs. - Aaron Berk; Yaniv Plan; Özgür Yilmaz (CoRR, 2018)

Deep generative demixing: Recovering Lipschitz signals from noisy subgaussian mixtures. - Aaron Berk (CoRR, 2020)

Deep Generative Demixing: Error Bounds for Demixing Subgaussian Mixtures of Lipschitz Signals. - Aaron Berk (ICASSP, 2021)

Learning from few examples: Classifying sex from retinal images via deep learning. - Aaron Berk; Gulcenur Ozturan; Parsa Delavari; David Maberley; Özgür Yilmaz; Ipek Oruc (CoRR, 2022)

On the Best Choice of Lasso Program Given Data Parameters. - Aaron Berk; Yaniv Plan; Özgür Yilmaz (IEEE Trans. Inf. Theory, 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)

Deep Proximal Gradient Method for Learned Convex Regularizers. - Aaron Berk; Yanting Ma; Petros Boufounos; Pu Wang 004; Hassan Mansour (ICASSP, 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)

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