559 citations · 1.5k across the 55 of their papers we have counts for
69 papers
Compressive Sensing - Introduction and Relations to Deep Learning
Hung-Hsu Chou, Johannes Maly, Holger Rauhut
Compressive sensing predicts that sparse vectors (signals) can be recovered from a small number of linear measurements via efficient algorithms. This finding, which dates back two…
High-Dimensional Confidence Regions in Sparse MRI
Frederik Hoppe, Felix Krahmer, Claudio Mayrink Verdun +2
One of the most promising solutions for uncertainty quantification in high-dimensional statistics is the debiased LASSO that relies on unconstrained -minimization. The init…
Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models
Leonardo Galli, Holger Rauhut, Mark Schmidt
Recent works have shown that line search methods can speed up Stochastic Gradient Descent (SGD) and Adam in modern over-parameterized settings. However, existing line searches may…
Robust Implicit Regularization via Weight Normalization
Hung-Hsu Chou, Holger Rauhut, Rachel Ward
Overparameterized models may have many interpolating solutions; implicit regularization refers to the hidden preference of a particular optimization method towards a certain interp…
Uncertainty quantification for sparse Fourier recovery
Frederik Hoppe, Felix Krahmer, Claudio Mayrink Verdun +2
One of the most prominent methods for uncertainty quantification in high-dimen-sional statistics is the desparsified LASSO that relies on unconstrained -minimization. The m…
Generalization Error Bounds for Iterative Recovery Algorithms Unfolded as Neural Networks
Ekkehard Schnoor, Arash Behboodi, Holger Rauhut
Motivated by the learned iterative soft thresholding algorithm (LISTA), we introduce a general class of neural networks suitable for sparse reconstruction from few linear measureme…