Leon Bungert
Leon Bungert
Institute of Mathematics, University of Würzburg
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Cited by
Cited by
Blind image fusion for hyperspectral imaging with the directional total variation
L Bungert, DA Coomes, MJ Ehrhardt, J Rasch, R Reisenhofer, ...
Inverse Problems 34 (4), 044003, 2018
Nonlinear spectral decompositions by gradient flows of one-homogeneous functionals
L Bungert, M Burger, A Chambolle, M Novaga
Analysis & PDE 14 (3), 823-860, 2021
Asymptotic Profiles of Nonlinear Homogeneous Evolution Equations of Gradient Flow Type
L Bungert, M Burger
Journal of Evolution Equations, 2019
CLIP: Cheap Lipschitz training of neural networks
L Bungert, R Raab, T Roith, L Schwinn, D Tenbrinck
International Conference on Scale Space and Variational Methods in Computer …, 2021
Continuum limit of Lipschitz learning on graphs
T Roith, L Bungert
Foundations of Computational Mathematics 23 (2), 393-431, 2023
Robust image reconstruction with misaligned structural information
L Bungert, MJ Ehrhardt
IEEE Access 8, 222944-222955, 2020
A Bregman learning framework for sparse neural networks
L Bungert, T Roith, D Tenbrinck, M Burger
Journal of Machine Learning Research 23 (192), 1-43, 2022
Solution paths of variational regularization methods for inverse problems
L Bungert, M Burger
Inverse Problems 35 (10), 105012, 2019
Uniform convergence rates for Lipschitz learning on graphs
L Bungert, J Calder, T Roith
IMA Journal of Numerical Analysis 43 (4), 2445-2495, 2023
The geometry of adversarial training in binary classification
L Bungert, N García Trillos, R Murray
Information and Inference: A Journal of the IMA 12 (2), 921-968, 2023
Eigenvalue problems in 𝐿^{∞}: optimality conditions, duality, and relations with optimal transport
L Bungert, Y Korolev
Communications of the American Mathematical Society 2 (08), 345-373, 2022
Computing nonlinear eigenfunctions via gradient flow extinction
L Bungert, M Burger, D Tenbrinck
Scale Space and Variational Methods in Computer Vision: 7th International …, 2019
Identifying untrustworthy predictions in neural networks by geometric gradient analysis
L Schwinn, A Nguyen, R Raab, L Bungert, D Tenbrinck, D Zanca, ...
Uncertainty in Artificial Intelligence, 854-864, 2021
Improving robustness against real-world and worst-case distribution shifts through decision region quantification
L Schwinn, L Bungert, A Nguyen, R Raab, F Pulsmeyer, D Precup, ...
International Conference on Machine Learning, 19434-19449, 2022
Variational regularisation for inverse problems with imperfect forward operators and general noise models
L Bungert, M Burger, Y Korolev, CB Schönlieb
Inverse Problems 36 (12), 125014, 2020
Structural analysis of an L-infinity variational problem and relations to distance functions
L Bungert, Y Korolev, M Burger
Pure and Applied Analysis 2 (3), 703-738, 2020
Polarized consensus-based dynamics for optimization and sampling
L Bungert, T Roith, P Wacker
Mathematical Programming, 1-31, 2024
Complete deterministic dynamics and spectral decomposition of the linear ensemble Kalman inversion
L Bungert, P Wacker
SIAM/ASA Journal on Uncertainty Quantification 11 (1), 320-357, 2023
Gamma-convergence of a nonlocal perimeter arising in adversarial machine learning
L Bungert, K Stinson
Calculus of Variations and Partial Differential Equations 63 (5), 114, 2024
Ratio convergence rates for Euclidean first-passage percolation: Applications to the graph infinity Laplacian
L Bungert, J Calder, T Roith
The Annals of Applied Probability, 2024
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