no code implementations • 23 Aug 2021 • Andreas Oslandsbotn, Zeljko Kereta, Valeriya Naumova, Yoav Freund, Alexander Cloninger
With a novel sub-sampling scheme, StreaMRAK reduces memory and computational complexities by creating a sketch of the original data, where the sub-sampling density is adapted to the bandwidth of the kernel and the local dimensionality of the data.
no code implementations • 7 Aug 2019 • Zeljko Kereta, Johannes Maly, Valeriya Naumova
In this work we consider numerical efficiency and convergence rates for solvers of non-convex multi-penalty formulations when reconstructing sparse signals from noisy linear measurements.
Information Theory Information Theory
1 code implementation • 24 Feb 2019 • Zeljko Kereta, Timo Klock, Valeriya Naumova
This paper deals with a nonlinear generalization of this framework to allow for a regressor that uses multiple index vectors, adapting to local changes in the responses.
no code implementations • 18 Jan 2018 • Massimo Fornasier, Johannes Maly, Valeriya Naumova
By adapting the concept of restricted isometry property from compressed sensing to our novel model class, we prove error bounds between global minimizers and ground truth, up to noise level, from a number of subgaussian measurements scaling as $R(s_1+s_2)$, up to log-factors in the dimension, and relative-to-diameter distortion.
Numerical Analysis Numerical Analysis
1 code implementation • 11 Oct 2017 • Markus Grasmair, Timo Klock, Valeriya Naumova
Another advantage of our algorithm is that it provides an overview on the solution stability over the whole range of parameters.
no code implementations • 13 Jan 2017 • Valeriya Naumova, Karin Schnass
This paper extends the recently proposed and theoretically justified iterative thresholding and $K$ residual means algorithm ITKrM to learning dicionaries from incomplete/masked training data (ITKrMM).