254 citations · 1.2k across the 51 of their papers we have counts for
54 papers
Generalization and Estimation Error Bounds for Model-based Neural Networks
Avner Shultzman, Eyar Azar, Miguel R. D. Rodrigues +1
Model-based neural networks provide unparalleled performance for various tasks, such as sparse coding and compressed sensing problems. Due to the strong connection with the sensing…
Information-theoretic Characterizations of Generalization Error for the Gibbs Algorithm
Gholamali Aminian, Yuheng Bu, Laura Toni +2
Various approaches have been developed to upper bound the generalization error of a supervised learning algorithm. However, existing bounds are often loose and even vacuous when ev…
Learning Algorithm Generalization Error Bounds via Auxiliary Distributions
Gholamali Aminian, Saeed Masiha, Laura Toni +1
Generalization error bounds are essential for comprehending how well machine learning models work. In this work, we suggest a novel method, i.e., the Auxiliary Distribution Method,…
Theoretical Perspectives on Deep Learning Methods in Inverse Problems
Jonathan Scarlett, Reinhard Heckel, Miguel R. D. Rodrigues +2
In recent years, there have been significant advances in the use of deep learning methods in inverse problems such as denoising, compressive sensing, inpainting, and super-resoluti…
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift
Gholamali Aminian, Mahed Abroshan, Mohammad Mahdi Khalili +2
A common assumption in semi-supervised learning is that the labeled, unlabeled, and test data are drawn from the same distribution. However, this assumption is not satisfied in man…
Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover
Wei Pu, Chao Zhou, Yonina C. Eldar +1
In this paper, we consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications. Specifically, we treat sensing problems with model…