59 citations · 80 across the 21 of their papers we have counts for
5 papers · 1 filter
Sparse Harmonic Transforms: A New Class of Sublinear-time Algorithms for Learning Functions of Many Variables
Bosu Choi, Mark Iwen, Felix Krahmer
We develop fast and memory efficient numerical methods for learning functions of many variables that admit sparse representations in terms of general bounded orthonormal tensor pro…
On Recovery Guarantees for One-Bit Compressed Sensing on Manifolds
Mark A. Iwen, Felix Krahmer, Sara Krause-Solberg +1
This paper studies the problem of recovering a signal from one-bit compressed sensing measurements under a manifold model; that is, assuming that the signal lies on or near a manif…
Are good local minima wide in sparse recovery?
Michael Moeller, Otmar Loffeld, Juergen Gall +1
The idea of compressed sensing is to exploit representations in suitable (overcomplete) dictionaries that allow to recover signals far beyond the Nyquist rate provided that they ad…
A Quotient Property for Matrices with Heavy-Tailed Entries and its Application to Noise-Blind Compressed Sensing
Felix Krahmer, Christian Kümmerle, Holger Rauhut
For a large class of random matrices with i.i.d. entries we show that the -quotient property holds with probability exponentially close to 1. In contrast to previous re…
Sparse Power Factorization: Balancing peakiness and sample complexity
Jakob Geppert, Felix Krahmer, Dominik Stöger
In many applications, one is faced with an inverse problem, where the known signal depends in a bilinear way on two unknown input vectors. Often at least one of the input vectors i…