Publications and Presentations
A list of my publications, preprints and presentations.
Prints
1.
T. Roith. Consistency, Robustness and Sparsity for Learning Algorithms.
(2024)
[Print]
[Preprint]
2.
S. Kabri, T. Roith, D. Tenbrinck, M. Burger. Resolution-invariant image classification based on Fourier neural operators.
(2023)
[Print]
[Preprint]
3.
L. Bungert, J.Calder, T. Roith. Uniform Convergence Rates for Lipschitz Learning on Graphs.
(2022)
[Print]
[Preprint]
4.
L. Bungert, R. Raab, T. Roith, L. Schwinn, D. Tenbrinck. CLIP: Cheap Lipschitz Training of Neural Networks.
(2021)
[Print]
[Preprint]
5.
L. Bungert, T. Roith, D. Tenbrinck, M. Burger. A Bregman Learning Framework for Sparse Neural Networks.
(2021)
[Print]
[Preprint]
6.
T. Roith, L. Bungert. Continuum Limit of Lipschitz Learning on Graphs.
(2020)
[Print]
[Preprint]
Preprints
1.
R. Bailo, A. Barbaro, S. N Gomes, K. Riedl, T. Roith, C. Totzeck, U. Vaes. CBX: Python and Julia packages for consensus-based interacting particle methods.
(2024)
[Preprint]
2.
T.J. Heeringa, T. Roith, C. Brune, M. Burger. Learning a Sparse Representation of Barron Functions with the Inverse Scale Space Flow.
(2023)
[Preprint]
3.
L. Bungert, T. Roith, P. Wacker. Polarized consensus-based dynamics for optimization and sampling.
(2022)
[Preprint]
4.
L. Bungert, T. Roith, D. Tenbrinck, M. Burger. Neural Architecture Search via Bregman Iterations.
(2021)
[Preprint]
Presentations
1.
Young applied mathematicians conference, Siena.
Oral Presentation.
Resolution-invariant image classification via FNOs.
(2023)
2.
Young applied mathematicians conference, Siena.
Oral Presentation.
Polarized consensus-based dynamics for optimization and sampling.
(2023)
3.
Digital Total.
Poster.
Computational Imaging@DESY.
(2023)
4.
MCQMC - International Conference on Monte Carlo and Quasi-Monte Carlo Methods.
Oral Presentation.
Kernelized Consensus Based Optimization.
(2022)
5.
SIAM - IS22 - Minisymposium Recent Advances on Stable Neural Networks.
Oral Presentation.
Stable Machine Learning via Lipschitz Methods.
(2022)
[YouTube]
6.
HCM - Workshop Synergies between Data Science and PDE Analysis.
Oral Presentation.
Uniform Convergence Rates for Lipschitz Learning.
(2022)
7.
GAMM - 92nd Annual Meeting.
Oral Presentation.
Uniform Convergence Rates for Lipschitz Learning.
(2022)
8.
ECOM - East Coast Optimization Meeting.
Oral Presentation.
A Bregman Learning Framework for Sparse Neural Networks.
(2022)
[Recording via GMU.edu]
9.
Conference on Calculus of Variation.
Oral Presentation.
Uniform Convergence Rates for Lipschitz Learning.
(2022)
10.
WWU Münster: Winterschool on Analysis and Applied Mathematics 2021.
Poster Session.
Continuum Limit of Lipschitz Learning on Graphs (Poster).
(2021)
[Poster]
11.
SSVM: International Conference on Scale Space and Variational Methods in Computer Vision.
Oral Presentation.
CLIP: Cheap Lipschitz Training of Neural Networks.
(2021)
[Slides (via unicloud.unicaen)]
12.
IMA Workshop: Theory and Algorithms in Graph-Based Learning.
Oral Presentation.
L-Infinity Variational Problems on Graphs: Applications and Continuum Limits.
(2020)
[YouTube, jointly with Leon Bungert]
