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I'm a PhD student in the Computer Vision Group at the Technical University of Munich, advised by Prof. Daniel Cremers. Before that, I obtained my Master's (2014) and Bachelor's degree (2011) in computer science at TU Munich.

My current interests include:

• Variational methods, optimization problems on measures, optimal transport.

• Probabilistic generative models, Bayesian deep learning, variational inference.

• Algorithms for continuous optimization (convex and nonconvex).

• Applications in machine learning, computer vision and image processing.

Publications

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. [pdf] [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. [pdf]

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. [pdf] [talk]

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

2018

B. Haefner, T. Möllenhoff, Y. Queau, 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. [pdf] [code]

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

2017

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

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. [pdf] [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. [pdf] [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. [pdf] [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. [pdf]

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. [pdf]

* contribution.

Code

• prost – C++ / CUDA implementation of a first-order primal-dual algorithm. Bindings are only for MATLAB. Developed with my colleague Emanuel Laude in 2016 at TU Munich.