Preprints

  1. Bungert, L., and Korolev, Y. (2025). Introduction to Nonlinear Spectral Analysis.
  2. Grossmann, T. G., Dittmer, S., Korolev, Y., and Schönlieb, C.-B. (2022). Unsupervised Learning of the Total Variation Flow.

Journal Articles

  1. Bartolucci, F., Carioni, M., Iglesias, J. A., Korolev, Y., Naldi, E., and Vigogna, S. (2026). A Lipschitz spaces view of infinitely wide shallow neural networks. SIAM Journal on Mathematical Analysis, (39 pages).
  2. Burger, M., Kabri, S., Korolev, Y., Roith, T., and Weigand, L. (2025). Analysis of mean-field models arising from self-attention dynamics in transformer architectures with layer normalization. Philosophical Transactions of the Royal Society A, 383(2298), 20240233 (48 pages). https://doi.org/10.1098/rsta.2024.0233
  3. Bredies, K., Carioni, M., Holler, M., Korolev, Y., and Schönlieb, C.-B. (2025). A sparse optimization approach to infinite infimal convolution regularization. Numerische Mathematik, 157(1), 41–96. https://doi.org/10.1007/s00211-024-01439-2
  4. Toader, B., Boulanger, J., Korolev, Y., Lenz, M. O., Manton, J., Schönlieb, C.-B., and Mureşan, L. (2022). Image Reconstruction in Light-Sheet Microscopy: Spatially Varying Deconvolution and Mixed Noise. Journal of Mathematical Imaging and Vision, 64(9), 968–992. https://doi.org/10.1007/s10851-022-01100-3
  5. Bungert, L., and Korolev, Y. (2022). Eigenvalue Problems in L^∞: Optimality Conditions, Duality, and Relations with Optimal Transport. Communications of the American Mathematical Society, 2, 345–373.
  6. Korolev, Y. (2022). Two-layer neural networks with values in a Banach space. SIAM Journal on Mathematical Analysis, 54(6), 6358–6389.
  7. Bungert, L., Burger, M., Korolev, Y., and Schönlieb, C.-B. (2020). Variational regularisation for inverse problems with imperfect forward operators and general noise models. Inverse Problems, 36(12), 125014 (32 pages).
  8. Aspri, A., Korolev, Y., and Scherzer, O. (2020). Data driven regularisation by projection. Inverse Problems, 36(12), 125009 (35 pages).
  9. Bungert, L., Korolev, Y., and Burger, M. (2020). Structural analysis of an L-infinity variational problem and relations to distance functions. Pure and Applied Analysis, 2(3), 703–738.
  10. Burger, M., Korolev, Y., and Rasch, J. (2019). Convergence rates and structure of solutions of inverse problems with imperfect forward models. Inverse Problems, 35(2), 024006 (33 pages). https://doi.org/10.1088/1361-6420/aaf6f5
  11. Korolev, Y., and Lellmann, J. (2018). Image Reconstruction with Imperfect Forward Models and Applications in Deblurring. SIAM Journal on Imaging Sciences, 11(1), 197–218. https://doi.org/10.1137/17M1141965
  12. Gorokh, A., Korolev, Y., and Valkonen, T. (2016). Diffusion Tensor Imaging with Deterministic Error Bounds. Journal of Mathematical Imaging and Vision, 56(1), 137–157. https://doi.org/10.1007/s10851-016-0639-7
  13. Korolev, Y. (2014). Making use of a partial order in solving inverse problems: II. Inverse Problems, 30(8), 085003 (10 pages). https://doi.org/10.1088/0266-5611/30/8/085003
  14. Korolev, Y., and Yagola, A. (2013). Making use of a partial order in solving inverse problems. Inverse Problems, 29(9), 095012 (12 pages). https://doi.org/10.1088/0266-5611/29/9/095012
  15. Korolev, Y., and Yagola, A. (2012). Error estimation in linear ill-posed problems with prior information. Computational Methods and Programming, 13, 14–18.
  16. Korolev, Y., Kubo, H., and Yagola, A. (2012). Parameter identification problem for a parabolic equation – application to the Black–Scholes option pricing model. Journal of Inverse and Ill-Posed Problems, 20(3), 327–337.
  17. Korolev, Y., and Yagola, A. (2012). On inverse problems in partially ordered spaces with a priori information. Journal of Inverse and Ill-Posed Problems, 20(4), 567–573.
  18. Korolev, Y., and Golubtsov, P. (2010). Two-level competition systems in common resource management problems. Mathematical Game Theory and Applications, 2(4), 25–51.

Conferences and Workshops

  1. Salehi, M. S., Bubba, T. A., and Korolev, Y. (2025). Fast Inexact Bilevel Optimization for Analytical Deep Image Priors. Scale Space and Variational Methods in Computer Vision, 30–42. Springer.
  2. Grossmann, T. G., Korolev, Y., Gilboa, G., and Schoenlieb, C. (2020). Deeply Learned Spectral Total Variation Decomposition. Advances in Neural Information Processing Systems, 33, 12115–12126. Curran Associates, Inc. Retrieved from https://proceedings.neurips.cc/paper/2020/file/8d3215ae97598264ad6529613774a038-Paper.pdf
  3. Burger, M., Korolev, Y., Schönlieb, C.-B., and Stollenwerk, C. (2019). A total variation based regularizer promoting piecewise-Lipschitz reconstructions. Scale Space and Variational Methods in Computer Vision, 485–497. Springer.
  4. Toropov, V., Korolev, Y., Barkalov, K., Kozinov, E., and Gergel, V. (2019). HPC Implementation of the Multipoint Approximation Method for Large Scale Design Optimization Problems Under Uncertainty. 6th International Conference on Engineering Optimization, 296–306. Springer.
  5. Korolev, Y., Toropov, V., and Shahpar, S. (2017). Design Optimization Under Uncertainty Using the Multipoint Approximation Method. 19th AIAA Non-Deterministic Approaches Conference.
  6. Korolev, Y., and Toropov, V. (2015). The Multipoint Approximation Method as a parallel optimisation framework for problems with computationally expensive responses. 4th International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering.
  7. Korolev, Y., Toropov, V., and Karabasov, S. (2015). Automatic Optimizer vs Human Optimizer for Low-Order Jet Noise Modelling. 21st AIAA/CEAS Aeroacoustics Conference.
  8. Korolev, Y., Toropov, V., and Shahpar, S. (2015). Large-scale CFD Optimisation based on the FFD Parametrisation using the Multipoint Approximation Method in an HPC Environment. 16th AIAA/ISSMO Multidisciplinary Analysis and Optimisation Conference.
  9. Korolev, Y., Yagola, A., Johnson, J., and Brinkerhoff, D. (2013). Methods of error estimation in inverse problems on compact sets in Banach lattices — theory and applications in ice sheet modeling. 4th Inverse Problems, Design and Optimisation Symposium.
  10. Yagola, A., and Korolev, Y. (2012). Error estimations in linear inverse problems in ordered spaces. 8th Congress of the International Society for Analysis, Its Applications, and Computations, 2.
  11. Yagola, A., and Korolev, Y. (2011). Error estimations in linear inverse problems with a priori information. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2.
  12. Korolev, Y., and Golubtsov, P. (2009). Modelling of common resource management problems. Proceedings of the “Lomonosov Readings” Conference.

Book Chapters

  1. Arridge, S., Hauptmann, A., and Korolev, Y. (2024). Inverse problems with learned forward operators. In T. A. Bubba (Ed.), Data-driven Models in Inverse Problems (pp. 73–106). De Gruyter. https://doi.org/doi:10.1515/9783111251233-003
  2. Aspri, A., Frischauf, L., Korolev, Y., and Scherzer, O. (2022). Data driven reconstruction using frames and Riesz bases. In B. Jadamba et al. (Ed.), Deterministic and Stochastic Optimal Control and Inverse Problems. CRC Press.
  3. Yagola, A., and Korolev, Y. (2013). Error estimation in ill-posed problems in special cases. In L. Beilina (Ed.), Applied Inverse Problems. Springer.