167 citations · 800 across the 50 of their papers we have counts for
65 papers
Fast One-Pass Sparse Approximation of the Top Eigenvectors of Huge Approximately Low-Rank Matrices? Yes, !
Edem Boahen, Simone Brugiapaglia, Hung-Hsu Chou +2
Motivated by applications such as sparse PCA, in this paper we present provably-accurate one-pass algorithms for the sparse approximation of the top eigenvectors of extremely massi…
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…
Uncertainty quantification for learned ISTA
Frederik Hoppe, Claudio Mayrink Verdun, Felix Krahmer +2
Model-based deep learning solutions to inverse problems have attracted increasing attention in recent years as they bridge state-of-the-art numerical performance with interpretabil…
Approximating Positive Homogeneous Functions with Scale Invariant Neural Networks
Stefan Bamberger, Reinhard Heckel, Felix Krahmer
We investigate to what extent it is possible to solve linear inverse problems with networks. Due to the scaling invariance arising from the linearity, an optimal reconstruct…
On the reconstruction of bandlimited signals from random samples quantized via noise-shaping
Rohan Joy, Felix Krahmer, Alessandro Lupoli +1
Noise-shaping quantization techniques are widely used for converting bandlimited signals from the analog to the digital domain. They work by ``shaping" the quantization noise so th…
How robust is randomized blind deconvolution via nuclear norm minimization against adversarial noise?
Julia Kostin, Felix Krahmer, Dominik Stöger
In this paper, we study the problem of recovering two unknown signals from their convolution, which is commonly referred to as blind deconvolution. Reformulation of blind deconvolu…
Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis
Maternus Herold, Anna Veselovska, Jonas Jehle +1
Efficient surrogate modelling is a key requirement for uncertainty quantification in data-driven scenarios. In this work, a novel approach of using Sparse Random Features for surro…
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…
Enhanced Digital Halftoning via Weighted Sigma-Delta Modulation
Felix Krahmer, Anna Veselovska
In this paper, we study error diffusion techniques for digital halftoning from the perspective of 1-bit Sigma-Delta quantization. We introduce a method to generate Sigma-Delta sche…
The Surprising Benefits of Hysteresis in Unlimited Sampling: Theory, Algorithms and Experiments
Dorian Florescu, Felix Krahmer, Ayush Bhandari
The Unlimited Sensing Framework (USF) was recently introduced to overcome the sensor saturation bottleneck in conventional digital acquisition systems. At its core, the USF allows…
Johnson-Lindenstrauss Embeddings with Kronecker Structure
Stefan Bamberger, Felix Krahmer, Rachel Ward
We prove the Johnson-Lindenstrauss property for matrices where has the restricted isometry property and is a diagonal matrix containing the entries of a Kronecker…
The Hanson-Wright Inequality for Random Tensors
Stefan Bamberger, Felix Krahmer, Rachel Ward
We provide moment bounds for expressions of the type where denotes the Kronecker pro…
Proof methods for robust low-rank matrix recovery
Tim Fuchs, David Gross, Peter Jung +3
Low-rank matrix recovery problems arise naturally as mathematical formulations of various inverse problems, such as matrix completion, blind deconvolution, and phase retrieval. Ove…
Sketching with Kerdock's crayons: Fast sparsifying transforms for arbitrary linear maps
Tim Fuchs, David Gross, Felix Krahmer +2
Given an arbitrary matrix , we consider the fundamental problem of computing for any such that is -sparse. While fast al…
Unlimited Sampling from Theory to Practice: Fourier-Prony Recovery and Prototype ADC
Ayush Bhandari, Felix Krahmer, Thomas Poskitt
Following the Unlimited Sampling strategy to alleviate the omnipresent dynamic range barrier, we study the problem of recovering a bandlimited signal from point-wise modulo samples…
The Modulo Radon Transform: Theory, Algorithms and Applications
Matthias Beckmann, Ayush Bhandari, Felix Krahmer
Recently, experiments have been reported where researchers were able to perform high dynamic range (HDR) tomography in a heuristic fashion, by fusing multiple tomographic projectio…
Riemannian thresholding methods for row-sparse and low-rank matrix recovery
Henrik Eisenmann, Felix Krahmer, Max Pfeffer +1
In this paper, we present modifications of the iterative hard thresholding (IHT) method for recovery of jointly row-sparse and low-rank matrices. In particular a Riemannian version…
On the robustness of noise-blind low-rank recovery from rank-one measurements
Felix Krahmer, Christian Kümmerle, Oleh Melnyk
We prove new results about the robustness of well-known convex noise-blind optimization formulations for the reconstruction of low-rank matrices from underdetermined linear measure…
A sample efficient sparse FFT for arbitrary frequency candidate sets in high dimensions
Lutz Kämmerer, Felix Krahmer, Toni Volkmer
In this paper a sublinear time algorithm is presented for the reconstruction of functions that can be represented by just few out of a potentially large candidate set of Fourier ba…
High order low-bit Sigma-Delta quantization for fusion frames
Zhen Gao, Felix Krahmer, Alexander M. Powell
We construct high order low-bit Sigma-Delta quantizers for the vector-valued setting of fusion frames. We prove that these quantizers can be stably implemented to quant…
On recovery guarantees for angular synchronization
Frank Filbir, Felix Krahmer, Oleh Melnyk
The angular synchronization problem of estimating a set of unknown angles from their known noisy pairwise differences arises in various applications. It can be reformulated as a op…
Well conditioned ptychograpic imaging via lost subspace completion
Anton Forstner, Felix Krahmer, Oleh Melnyk +1
Ptychography, a special case of the phase retrieval problem, is a popular method in modern imaging. Its measurements are based on the shifts of a locally supported window function.…
One-Bit Sigma-Delta modulation on the circle
Olga Graf, Felix Krahmer, Sara Krause-Solberg
Manifold models in data analysis and signal processing have become more prominent in recent years. In this paper, we will look at one of the main tasks of modern signal processing,…
Predicting sparse circle maps from their dynamics
Felix Krahmer, Christian Kühn, Nada Sissouno
The problem of identifying a dynamical system from its dynamics is of great importance for many applications. Recently it has been suggested to impose sparsity models for improved…