Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Nicole Mücke"'
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 9 (2023)
In this study, we consider algorithm unfolding for the multiple measurement vector (MMV) problem in the case where only few training samples are available. Algorithm unfolding has been shown to empirically speed-up in a data-driven way the convergenc
Externí odkaz:
https://doaj.org/article/7d8fa745c5aa4ac1a499ae716f500048
Autor:
Gilles Blanchard, Nicole Mücke
Publikováno v:
Analysis and Applications
Analysis and Applications, World Scientific Publishing, 2020, 18 (4), pp.683-696. ⟨10.1142/S0219530519500258⟩
Analysis and Applications, World Scientific Publishing, 2020, 18 (04), pp.683-696. ⟨10.1142/S0219530519500258⟩
Analysis and Applications, World Scientific Publishing, 2020, 18 (4), pp.683-696. ⟨10.1142/S0219530519500258⟩
Analysis and Applications, World Scientific Publishing, 2020, 18 (04), pp.683-696. ⟨10.1142/S0219530519500258⟩
International audience; We investigate if kernel regularization methods can achieve minimax convergence rates over a source condition regularity assumption for the target function. These questions have been considered in past literature, but only und
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33338ab5847548c49737330d443385fe
https://doi.org/10.1142/s0219530519500258
https://doi.org/10.1142/s0219530519500258
Optimization in machine learning typically deals with the minimization of empirical objectives defined by training data. The ultimate goal of learning, however, is to minimize the error on future data (test error), for which the training data provide
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e163e7119fea93f42a5d9dea8a50424e
We study reproducing kernel Hilbert spaces (RKHS) on a Riemannian manifold. In particular, we discuss under which condition Sobolev spaces are RKHS and characterize their reproducing kernels. Further, we introduce and discuss a class of smoother RKHS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::99d26a68f05c6dde72a8e2892cbb28af
Autor:
Gilles Blanchard, Nicole Mücke
Publikováno v:
Foundations of Computational Mathematics
Foundations of Computational Mathematics, Springer Verlag, 2018, 18 (4), pp.971-1013. ⟨10.1007/s10208-017-9359-7⟩
Foundations of Computational Mathematics, Springer Verlag, 2018, 18 (4), pp.971-1013. ⟨10.1007/s10208-017-9359-7⟩
We consider a statistical inverse learning (also called inverse regression) problem, where we observe the image of a function f through a linear operator A at i.i.d. random design points $$X_i$$ , superposed with an additive noise. The distribution o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18705369f0b9514814b8b4bcf3a0e41e
https://hal-universite-paris-saclay.archives-ouvertes.fr/hal-03327425
https://hal-universite-paris-saclay.archives-ouvertes.fr/hal-03327425
Autor:
Stankewitz, Bernhard1 (AUTHOR) stankebe@math.hu-berlin.de, Mücke, Nicole2 (AUTHOR), Rosasco, Lorenzo3,4 (AUTHOR)
Publikováno v:
Computational Optimization & Applications. Jan2023, Vol. 84 Issue 1, p265-294. 30p.
Publikováno v:
Frontiers in Applied Mathematics & Statistics; 2023, p01-12, 12p
Autor:
Blanchard, Gilles1 blanchard@uni-potsdam.de, Mücke, Nicole1 nmuecke@uni-potsdam.de
Publikováno v:
Foundations of Computational Mathematics. Aug2018, Vol. 18 Issue 4, p971-1013. 43p.
Autor:
Filippo De Mari, Ernesto De Vito
Deep connections exist between harmonic and applied analysis and the diverse yet connected topics of machine learning, data analysis, and imaging science. This volume explores these rapidly growing areas and features contributions presented at the se