Leon Bungert

According to our database, Leon Bungert authored at least 23 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

The Infinity Laplacian Eigenvalue Problem: Reformulation and a Numerical Scheme.

Farid Bozorgnia, Leon Bungert, Daniel Tenbrinck

J. Sci. Comput., February, 2024

2023

Continuum Limit of Lipschitz Learning on Graphs.

Tim Roith, Leon Bungert

Found. Comput. Math., April, 2023

Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion.

Leon Bungert, Philipp Wacker

SIAM/ASA J. Uncertain. Quantification, March, 2023

It begins with a boundary: A geometric view on probabilistically robust learning.

Leon Bungert, Nicolás García Trillos, Matt Jacobs, Daniel McKenzie, Dorde Nikolic, Qingsong Wang

CoRR, 2023

2022

A Bregman Learning Framework for Sparse Neural Networks.

Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger

J. Mach. Learn. Res., 2022

Gamma-convergence of a nonlocal perimeter arising in adversarial machine learning.

Leon Bungert, Kerrek Stinson

CoRR, 2022

Polarized consensus-based dynamics for optimization and sampling.

Leon Bungert, Tim Roith, Philipp Wacker

CoRR, 2022

Ratio convergence rates for Euclidean first-passage percolation: Applications to the graph infinity Laplacian.

Leon Bungert, Jeff Calder, Tim Roith

CoRR, 2022

Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification.

Leo Schwinn, Leon Bungert, An Nguyen, René Raab, Falk Pulsmeyer, Doina Precup, Bjoern M. Eskofier, Dario Zanca

Proceedings of the International Conference on Machine Learning, 2022

2021

Nonlinear Power Method for Computing Eigenvectors of Proximal Operators and Neural Networks.

Leon Bungert, Ester Hait-Fraenkel, Nicolas Papadakis, Guy Gilboa

SIAM J. Imaging Sci., 2021

The Geometry of Adversarial Training in Binary Classification.

Leon Bungert, Nicolás García Trillos, Ryan Murray

CoRR, 2021

Uniform Convergence Rates for Lipschitz Learning on Graphs.

Leon Bungert, Jeff Calder, Tim Roith

CoRR, 2021

Neural Architecture Search via Bregman Iterations.

Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger

CoRR, 2021

Gradient Flows, Nonlinear Power Methods, and Computation of Nonlinear Eigenfunctions.

Leon Bungert, Martin Burger

CoRR, 2021

Complete Dynamics and Spectral Decomposition of the Ensemble Kalman Inversion.

Leon Bungert, Philipp Wacker

CoRR, 2021

Identifying untrustworthy predictions in neural networks by geometric gradient analysis.

Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Björn M. Eskofier

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

CLIP: Cheap Lipschitz Training of Neural Networks.

Leon Bungert, René Raab, Tim Roith, Leo Schwinn, Daniel Tenbrinck

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2021

2020

Variational regularisation for inverse problems with imperfect forward operators and general noise models.

Leon Bungert, Martin Burger, Yury Korolev, Carola-Bibiane Schoenlieb

CoRR, 2020

Robust Image Reconstruction With Misaligned Structural Information.

Leon Bungert, Matthias J. Ehrhardt

IEEE Access, 2020

2019

Computing Nonlinear Eigenfunctions via Gradient Flow Extinction.

Leon Bungert, Martin Burger, Daniel Tenbrinck

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2019

2018

Comparison of two local discontinuous Galerkin formulations for the subjective surfaces problem.

Vadym Aizinger, Leon Bungert, Michael Fried

Comput. Vis. Sci., 2018

2017

A Discontinuous Galerkin Method for the Subjective Surfaces Problem.

Leon Bungert, Vadym Aizinger, Michael Fried

J. Math. Imaging Vis., 2017

Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation.

Leon Bungert, David A. Coomes, Matthias J. Ehrhardt, Jennifer Rasch, Rafael Reisenhofer, Carola-Bibiane Schönlieb

CoRR, 2017