1.1k citations · 6.2k across the 58 of their papers we have counts for
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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…
Note on the saturation of the norm inequalities between diamond and nuclear norm
Ulrich Michel, Martin Kliesch, Richard Kueng +1
The diamond norm plays an important role in quantum information and operator theory. Recently, it has also been proposed as a regularizer for low-rank matrix recovery. The norm con…
Low rank matrix recovery from Clifford orbits
Richard Kueng, Huangjun Zhu, David Gross
We prove that low-rank matrices can be recovered efficiently from a small number of measurements that are sampled from orbits of a certain matrix group. As a special case, our theo…
Improving compressed sensing with the diamond norm
Martin Kliesch, Richard Kueng, Jens Eisert +1
In low-rank matrix recovery, one aims to reconstruct a low-rank matrix from a minimal number of linear measurements. Within the paradigm of compressed sensing, this is made computa…
Improved Recovery Guarantees for Phase Retrieval from Coded Diffraction Patterns
David Gross, Felix Krahmer, Richard Kueng
In this work we analyze the problem of phase retrieval from Fourier measurements with random diffraction patterns. To this end, we consider the recently introduced PhaseLift algori…
A Partial Derandomization of PhaseLift using Spherical Designs
D. Gross, F. Krahmer, R. Kueng
The problem of retrieving phase information from amplitude measurements alone has appeared in many scientific disciplines over the last century. PhaseLift is a recently introduced…