## List of Publications

### preprints

### 2024

Y. Shen*, N. Daheim*, B. Cong, P. Nickl, G.M. Marconi, C. Bazan, R. Yokota, I. Gurevych, D. Cremers, M.E. Khan, T. Möllenhoff.
Variational Learning is Effective for Large Deep Networks.
*Proceedings of the International Conference on Machine Learning (ICML)*, 2024. [code]

N. Daheim, T. Möllenhoff, E. M. Ponti, I. Gurevych, M. E. Khan.
Model Merging by Uncertainty-Based Gradient Matching.
*Proceedings of the International Conference on Learning Representations (ICLR)*, 2024.

E. Guha, S. Natarajan, T. Möllenhoff, M. E. Khan, E. Ndiaye.
Conformal Prediction via Regression-as-Classification.
*Proceedings of the International Conference on Learning Representations (ICLR)*, 2024.

### 2023

P. Nickl, L. Xu, D. Tailor, T. Möllenhoff, M. E. Khan.
The Memory-Perturbation Equation: Understanding Models’ Sensitivity to Data. In
*Proceedings of the Conference on Neural Information Processing Systems (NeurIPS)*, 2023.

T. Möllenhoff, M. E. Khan.
SAM as an Optimal Relaxation of Bayes. In
*Proceedings of the International Conference on Learning Representations (ICLR)*, 2023. [code]

E. M. Kiral, T. Möllenhoff, M. E. Khan.
The Lie-Group Bayesian Learning Rule. In *Proceedings of the
International Conference on Artificial Intelligence and Statistics (AISTATS)*, 2023. [code]

Z. Ye, B. Haefner, Y. Quéau, T. Möllenhoff,
D. Cremers. A Cutting-Plane Method for Sublabel-Accurate Relaxation of Problems with Product Label Spaces,
*International Journal of Computer Vision (IJCV)*, 2023.

### 2022

H. Dröge, T. Möllenhoff, M. Moeller.
Non-smooth energy dissipating networks. *IEEE
Conference on Image Processing (ICIP)*, 2022.

H. Bauermeister*, E. Laude*, T. Möllenhoff, M. Moeller,
D. Cremers. Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable
Approach for Continuous Markov Random Fields. *SIAM
Journal on Imaging Sciences*, 2022. [published version]

### 2021

Z. Ye, B. Haefner, Y. Quéau, T. Möllenhoff,
D. Cremers. Sublabel-Accurate Multilabeling Meets Product Label Spaces. *Proceedings
of the DAGM German Conference on Pattern Recognition (GCPR)*, 2021.

### 2020

T. Möllenhoff. Efficient Lifting Methods for Variational Problems. *PhD Thesis, Technical University of Munich*, 2020.

Z. Ye, T. Möllenhoff, T. Wu, D. Cremers. Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning. In *Proceedings of the
International Conference on Artificial Intelligence and Statistics (AISTATS)*, 2020. [code]

### 2019

P. Bréchet, T. Wu, T. Möllenhoff, D. Cremers. Informative GANs via structured regularization of optimal transport. *Optimal Transport and Machine Learning (NeurIPS
Workshop)*, 2019.

T. Möllenhoff, D. Cremers. Lifting vectorial variational problems: A natural formulation based on geometric measure theory and discrete exterior calculus. In
*Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR)*, 2019. [talk]

T. Möllenhoff, D. Cremers. Flat metric minimization with applications in generative modeling. In *Proceedings of the International Conference on Machine Learning
(ICML)*, 2019. [code], [talk], [poster]

M. Moeller, T. Möllenhoff, D. Cremers. Controlling neural networks via energy dissipation. In *Proceedings of the International Conference on Computer Vision
(ICCV)*, 2019.

### 2018

B. Haefner, T. Möllenhoff, Y. Quéau, D. Cremers. Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading. In *Proceedings of the
Conference on Computer Vision and Pattern Recognition (CVPR)*, 2018. [code]

T. Frerix*, T. Möllenhoff*, M. Moeller*, D. Cremers. Proximal backpropagation. In *Proceedings of the International Conference on Learning Representations
(ICLR)*, 2018. [code]

T. Möllenhoff, Z. Ye, T. Wu, D. Cremers. Combinatorial preconditioners for proximal algorithms on graphs. In *Proceedings of the International Conference on
Artificial Intelligence and Statistics (AISTATS)*, 2018.

### 2017

T. Möllenhoff, D. Cremers. Sublabel-accurate discretization of nonconvex free-discontinuity problems. In *Proceedings of the International Conference on Computer
Vision (ICCV)*, 2017.

### 2016

E. Laude*, T. Möllenhoff*, M. Moeller, J. Lellmann, D. Cremers. Sublabel-accurate convex relaxation of vectorial multilabel energies. In *Proceedings of the European Conference on Computer
Vision (ECCV)*, 2016. [code]

T. Möllenhoff*, E. Laude*, M. Moeller, J. Lellmann, D. Cremers. Sublabel-accurate relaxation of nonconvex energies. In *Proceedings of the Conference on Computer Vision and Pattern Recognition
(CVPR)*, 2016. [code], [talk]

### 2015

T. Möllenhoff, E. Strekalovskiy, M. Moeller, D. Cremers. The primal-dual hybrid gradient method for semiconvex splittings. *SIAM Journal on Imaging Sciences*, 2015. [talk], [slides]

T. Möllenhoff, E. Strekalovskiy, M. Moeller, D. Cremers. Low rank priors for color image regularization. *International Workshop on Energy Minimization Methods in Computer
Vision and Pattern Recognition (EMMCVPR)*, 2015.

### 2013

T. Möllenhoff, E. Toeppe, C. Nieuwenhuis, D. Cremers. Efficient convex optimization for minimal partition problems with volume constraints. *International Workshop on Energy
Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)*, 2013.

* equal contribution.