CLIP: Cheap Lipschitz Training of Neural Networks. - Leon Bungert; René Raab; Tim Roith; Leo Schwinn; Daniel Tenbrinck (SSVM, 2021)

Neural Architecture Search via Bregman Iterations. - Leon Bungert; Tim Roith; Daniel Tenbrinck; Martin Burger 001 (CoRR, 2021)

Uniform Convergence Rates for Lipschitz Learning on Graphs. - Leon Bungert; Jeff Calder; Tim Roith (CoRR, 2021)

Ratio convergence rates for Euclidean first-passage percolation: Applications to the graph infinity Laplacian. - Leon Bungert; Jeff Calder; Tim Roith (CoRR, 2022)

A Bregman Learning Framework for Sparse Neural Networks. - Leon Bungert; Tim Roith; Daniel Tenbrinck; Martin Burger 001 (J. Mach. Learn. Res., 2022)

Resolution-Invariant Image Classification Based on Fourier Neural Operators. - Samira Kabri; Tim Roith; Daniel Tenbrinck; Martin Burger 001 (SSVM, 2023)

Learning a Sparse Representation of Barron Functions with the Inverse Scale Space Flow. - Tjeerd Jan Heeringa; Tim Roith; Christoph Brune; Martin Burger 001 (CoRR, 2023)

Continuum Limit of Lipschitz Learning on Graphs. - Tim Roith; Leon Bungert (Found. Comput. Math., 2023)

Adversarial flows: A gradient flow characterization of adversarial attacks. - Lukas Weigand; Tim Roith; Martin Burger 003 (CoRR, 2024)

CBX: Python and Julia Packages for Consensus-Based Interacting Particle Methods. - Rafael Bailo; Alethea Barbaro; Susana N. Gomes; Konstantin Riedl; Tim Roith; Claudia Totzeck; Urbain Vaes (J. Open Source Softw., 2024)

MirrorCBO: A consensus-based optimization method in the spirit of mirror descent. - Leon Bungert; Franca Hoffmann; Dohyeon Kim; Tim Roith (CoRR, 2025)

Polarized consensus-based dynamics for optimization and sampling. - Leon Bungert; Tim Roith; Philipp Wacker (Math. Program., 2025)

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies. - Tim Roith; Leon Bungert; Philipp Wacker (CoRR, 2025)