| Sublabel-accurate relaxation of nonconvex energies T Mollenhoff, E Laude, M Moeller, J Lellmann, D Cremers Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 50 | 2016 |
| Sublabel-accurate convex relaxation of vectorial multilabel energies E Laude, T Möllenhoff, M Moeller, J Lellmann, D Cremers Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 30 | 2016 |
| Bregman proximal mappings and Bregman–Moreau envelopes under relative prox-regularity E Laude, P Ochs, D Cremers Journal of Optimization Theory and Applications 184, 724-761, 2020 | 17 | 2020 |
| Lifting the convex conjugate in Lagrangian relaxations: A tractable approach for continuous Markov random fields H Bauermeister, E Laude, T Möllenhoff, M Moeller, D Cremers SIAM Journal on Imaging Sciences 15 (3), 1253-1281, 2022 | 11 | 2022 |
| A nonconvex proximal splitting algorithm under Moreau-Yosida regularization E Laude, T Wu, D Cremers International Conference on Artificial Intelligence and Statistics, 491-499, 2018 | 10 | 2018 |
| Bregman proximal gradient algorithms for deep matrix factorization MC Mukkamala, F Westerkamp, E Laude, D Cremers, P Ochs International Conference on Scale Space and Variational Methods in Computer …, 2021 | 9* | 2021 |
| Distributed photometric bundle adjustment N Demmel, M Gao, E Laude, T Wu, D Cremers 2020 International Conference on 3D Vision (3DV), 140-149, 2020 | 9 | 2020 |
| Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs E Laude, JH Lange, J Schüpfer, C Domokos, L Leal-Taixé, FR Schmidt, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 9* | 2018 |
| Optimization of inf-convolution regularized nonconvex composite problems E Laude, T Wu, D Cremers The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 6 | 2019 |
| Anisotropic proximal gradient E Laude, P Patrinos arXiv preprint arXiv:2210.15531, 2022 | 4 | 2022 |
| Dualities for non-Euclidean smoothness and strong convexity under the light of generalized conjugacy E Laude, A Themelis, P Patrinos SIAM Journal on Optimization 33 (4), 2721-2749, 2023 | 3* | 2023 |
| Adaptive proximal gradient methods are universal without approximation KA Oikonomidis, E Laude, P Latafat, A Themelis, P Patrinos arXiv preprint arXiv:2402.06271, 2024 | 2 | 2024 |
| Power proximal point and augmented Lagrangian method KA Oikonomidis, A Bodard, E Laude, P Patrinos arXiv preprint arXiv:2312.12205, 2023 | 2 | 2023 |
| Anisotropic proximal point algorithm E Laude, P Patrinos arXiv preprint arXiv:2312.09834, 2023 | 2 | 2023 |
| Lower envelopes and lifting for structured nonconvex optimization E Laude Technische Universität München, 2021 | 2 | 2021 |
| Convex relaxations for large-scale graphically structured nonconvex problems with spherical constraints: An optimal transport approach R Kenis, E Laude, P Patrinos arXiv preprint arXiv:2308.00079, 2023 | | 2023 |
| Convex Relaxations for Manifold-Valued Markov Random Fields with Approximation Guarantees R Kenis, E Laude, P Patrinos | | |
Daniel CremersTechnical University of MunichDirección de correo verificada de tum.de
