https://orcid.org/0000-0002-6339-652X
Research Interests
Inverse Problems, Variational Methods, Mathematical Imaging, Theoretical Machine Learning
Teaching
I am teaching Introduction to Nonlinear Spectral Analysis in Lent term 2021/2022.
I also taught Inverse Problems in 2018/2019, 2019/2020 and 2020/2021.
Personal Web Page
NB: the list of publications below is generated automatically and may be incomplete. For an up-to-date list and more details about my research and teaching please visit my personal web page yury-korolev.gitlab.io.
Publications
Variational regularisation for inverse problems with imperfect forward operators and general noise models.
– Inverse Probl
(2020)
36,
125014
(DOI: 10.1088/1361-6420/abc531)
Data driven regularization by projection
– Inverse Problems
(2020)
36,
125009
(DOI: 10.1088/1361-6420/abb61b)
Structural analysis of an $L$-infinity variational problem and relations
to distance functions
– Pure and Applied Analysis
(2020)
2,
703
(DOI: 10.2140/paa.2020.2.703)
Deeply learned spectral total variation decomposition
– Advances in Neural Information Processing Systems
(2020)
2020-December,
A Total Variation Based Regularizer Promoting Piecewise-Lipschitz Reconstructions
– Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(2019)
11603,
485
(DOI: 10.1007/978-3-030-22368-7_38)
Convergence rates and structure of solutions of inverse problems with imperfect forward models
– Inverse Problems
(2019)
35,
024006
(DOI: 10.1088/1361-6420/aaf6f5)
Total Variation Regularisation with Spatially Variable Lipschitz Constraints.
– CoRR
(2019)
abs/1912.02768,
HPC Implementation of the Multipoint Approximation Method for Large Scale Design Optimization Problems Under Uncertainty
(2018)
296
(DOI: 10.1007/978-3-319-97773-7_27)
Image Reconstruction with Imperfect Forward Models and Applications in Deblurring
– SIAM Journal on Imaging Sciences
(2018)
11,
197
(DOI: 10.1137/17m1141965)
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