piwik-script

English Intern
  • Gruppe von Studierenden am Campus
Mathematik des Maschinellen Lernens

Leon Bungert, Prof. Dr.

Prof. Dr. Leon Bungert

W2 Professor mit Tenure Track zu W3
Professur für Mathematik III (Mathematik des Maschinellen Lernens)
Emil-Fischer-Straße 40
97074 Würzburg
Gebäude: Mathematik Ost (40)
Raum: 01.008
Telefon: +49 931 31-82849
Fax: +49 931 31-81492

Private Website

Unter den folgenden Links finden Sie Informationen zu

Professor (Tenure Track) an der Universität Würzburg seit 2023

Frühere Positionen:

  • Nachwuchsgruppenleiter an der Technischen Universität Berlin (2023)
  • Postdoktorand am Hausdorff-Zentrum für Mathematik, Universität Bonn (2021 - 2023)
  • Postdoktorand an der Universität Erlangen-Nürnberg (2020 - 2021)

Akademische Ausbildung:

  • Promotion (summa cum laude) an der Universität Erlangen-Nürnberg (2020), Titel der Arbeit: "Nonlinear spectral theory with variational methods"
  • M.Sc. Mathematik an der Universität Erlangen-Nürnberg (2017)
  • B.Sc. Mathematik an der Universität Erlangen-Nürnberg (2016)

Publikationen

  • 1.
    It begins with a boundary: A geometric view on probabilistically robust learning
    Bungert, L., Trillos, N. G., Jacobs, M., McKenzie, D., Nikolić, Đorđe, Wang, Q.
    https://arxiv.org/abs/2305.18779 (2023)
  • 1.
    The convergence rate of $p$-harmonic to infinity-harmonic functions
    Bungert, L.
    https://arxiv.org/abs/2302.08462 (2023)
  • 1.
    Polarized consensus-based dynamics for optimization and sampling
    Bungert, L., Roith, T., Wacker, P.
    https://arxiv.org/abs/2211.05238 (2022)
  • 1.
    Gamma-convergence of a nonlocal perimeter arising in adversarial machine learning
    Bungert, L., Stinson, K.
    https://arxiv.org/abs/2211.15223 (2022)
  • 1.
    Ratio convergence rates for Euclidean first-passage percolation: Applications to the graph infinity Laplacian
    Bungert, L., Calder, J., Roith, T.
    https://arxiv.org/abs/2210.09023 (2022)
  • 1.
    Neural Architecture Search via Bregman Iterations
    Bungert, L., Roith, T., Tenbrinck, D., Burger, M.
    https://arxiv.org/abs/2106.02479 (2021)
  • 1.
    The infinity Laplacian eigenvalue problem: reformulation and a numerical scheme
    Bozorgnia, F., Bungert, L., Tenbrinck, D.
    https://arxiv.org/abs/2004.08127 (2020)
  • 1.
    The lion in the attic -- A resolution of the Borel--Kolmogorov paradox
    Bungert, L., Wacker, P.
    (2020)

  • 1.
    Uniform convergence rates for Lipschitz learning on graphs
    Bungert, L., Calder, J., Roith, T.
    IMA Journal of Numerical Analysis 43, 2445-2495 (2023)
  • 1.
    Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion
    Bungert, L., Wacker, P.
    SIAM/ASA Journal on Uncertainty Quantification (2023)
  • 1.
    The geometry of adversarial training in binary classification
    Bungert, L., García Trillos, N., Murray, R.
    Information and Inference: A Journal of the IMA 12, 921-968 (2023)
  • 1.
    The inhomogeneous $p$-Laplacian equation with Neumann boundary conditions in the limit $ ptoinfty$
    Bungert, L.
    Advances in Continuous and Discrete Models 2023, 1-17 (2023)
  • 1.
    A Bregman Learning Framework for Sparse Neural Networks
    Bungert, L., Roith, T., Tenbrinck, D., Burger, M.
    Journal of Machine Learning Research 23, 1-43 (2022)
  • 1.
    Eigenvalue problems in $mathrmL^infty$: optimality conditions, duality, and relations with optimal transport
    Bungert, L., Korolev, Y.
    Communications of the American Mathematical Society 2, 345–373 (2022)
  • 1.
    Continuum Limit of Lipschitz Learning on Graphs
    Roith, T., Bungert, L.
    Foundations of Computational Mathematics (2022)
  • 1.
    Nonlinear power method for computing eigenvectors of proximal operators and neural networks
    Bungert, L., Hait-Fraenkel, E., Papadakis, N., Gilboa, G.
    SIAM Journal on Imaging Sciences 14, 1114-1148 (2021)
  • 1.
    Nonlinear spectral decompositions by gradient flows of one-homogeneous functionals
    Bungert, L., Burger, M., Chambolle, A., Novaga, M.
    Analysis & PDE 14, 823-860 (2021)
  • 1.
    Structural analysis of an $L$-infinity variational problem and relations to distance functions
    Bungert, L., Korolev, Y., Burger, M.
    Pure and Applied Analysis 2, 703–738 (2020)
  • 1.
    Asymptotic profiles of nonlinear homogeneous evolution equations of gradient flow type
    Bungert, L., Burger, M.
    Journal of Evolution Equations 20, 1061-1092 (2020)
  • 1.
    Robust Image Reconstruction with Misaligned Structural Information
    Bungert, L., Ehrhardt, M. J.
    IEEE Access 8, 222944-222955 (2020)
  • 1.
    Localization of Passive 3-D Coils as an Inverse Problem: Theoretical Analysis and a Numerical Method
    Doß, M., Bungert, L., Cichon, D., Brauer, H., Psiuk, R.
    IEEE Transactions on Magnetics 56, 1-10 (2020)
  • 1.
    Variational regularisation for inverse problems with imperfect forward operators and general noise models
    Bungert, L., Burger, M., Korolev, Y., Schönlieb, C.-B.
    Inverse Problems 36, 125014 (2020)
  • 1.
    Solution paths of variational regularization methods for inverse problems
    Bungert, L., Burger, M.
    Inverse Problems 35, 105012 (2019)
  • 1.
    Blind image fusion for hyperspectral imaging with the directional total variation
    Bungert, L., Coomes, D. A., Ehrhardt, M. J., Rasch, J., Reisenhofer, R., Schönlieb, C.-B.
    Inverse Problems 34, 044003 (2018)
  • 1.
    Comparison of two local discontinuous Galerkin formulations for the subjective surfaces problem
    Aizinger, V., Bungert, L., Fried, M.
    Computing and Visualization in Science 18, 193-202 (2018)
  • 1.
    Robust Blind Image Fusion for Misaligned Hyperspectral Imaging Data
    Bungert, L., Ehrhardt, M. J., Reisenhofer, R.
    PAMM 18, e201800033 (2018)
  • 1.
    A discontinuous Galerkin method for the subjective surfaces problem
    Bungert, L., Aizinger, V., Fried, M.
    Journal of Mathematical Imaging and Vision 58, 147-161 (2017)

  • 1.
    Chapter 13 - Gradient flows and nonlinear power methods for the computation of nonlinear eigenfunctions
    Bungert, L., Burger, M.
    In: Trélat, E. and Zuazua, E. (eds.) Numerical Control: Part A. pp. 427-465. Elsevier (2022)
  • 1.
    Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
    Schwinn, L., Bungert, L., Nguyen, A., Raab, R., Pulsmeyer, F., Precup, D., Eskofier, B., Zanca, D.
    In: Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., and Sabato, S. (eds.) Proceedings of the 39th International Conference on Machine Learning. pp. 19434-19449. PMLR (2022)
  • 1.
    CLIP: Cheap Lipschitz Training of Neural Networks
    Bungert, L., Raab, R., Roith, T., Schwinn, L., Tenbrinck, D.
    In: Elmoataz, A., Fadili, J., Quéau, Y., Rabin, J., and Simon, L. (eds.) Scale Space and Variational Methods in Computer Vision. pp. 307-319. Springer International Publishing, Cham (2021)
  • 1.
    Identifying untrustworthy predictions in neural networks by geometric gradient analysis
    Schwinn, L., Nguyen, A., Raab, R., Bungert, L., Tenbrinck, D., Zanca, D., Burger, M., Eskofier, B.
    In: de Campos, C. and Maathuis, M. H. (eds.) Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence. pp. 854-864. PMLR (2021)
  • 1.
    Computing nonlinear eigenfunctions via gradient flow extinction
    Bungert, L., Burger, M., Tenbrinck, D.
    In: International Conference on Scale Space and Variational Methods in Computer Vision. pp. 291-302. Springer (2019)