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@inproceedings{moellenhoff-laude-cvpr16,
author = {T. Möllenhoff and E. Laude and M. Moeller and J. Lellmann and D. Cremers},
title = {Sublabel-Accurate Relaxation of Nonconvex Energies},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2016},
titleurl = {moellenhoff_laude_cvpr_16.pdf},
keywords = {convex-relaxation},
award = {Oral Presentation, Received the Best Paper Honorable Mention Award at CVPR 2016},
}
@inproceedings{laude16eccv,
author = {E. Laude and T. Möllenhoff and M. Moeller and J. Lellmann and D. Cremers},
title = {Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies},
year = {2016},
month = {October},
booktitle = {European Conference on Computer Vision (ECCV)},
keywords = {convex-optimization, convex-relaxation, multilabeling, primal-dual},
}
@article{laude2020jota,
title = {Bregman Proximal Mappings and Bregman-Moreau Envelopes under Relative Prox-Regularity},
author = {E. Laude and P. Ochs and D. Cremers},
journal = {Journal of Optimization Theory and Applications},
volume = {184},
number = {3},
pages = {724-761},
year = {2020},
eprint = {1907.04306},
eprinttype = {arXiv},
eprintclass = {math.OC},
}
@inproceedings{laude-et-al-transductive,
author = {E. Laude and J.-H. Lange and J. Schüpfer and C. Domokos and L. Leal-Taixé and F. R. Schmidt and B. Andres and D. Cremers},
title = {Discrete-Continuous {ADMM} for Transductive Inference in Higher-Order {MRF}s},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2018},
titleurl = {laude-2018-discrete-continuous.pdf},
}
@inproceedings{laude-et-al-nonconvex-moreau-17,
author = {E. Laude and T. Wu and D. Cremers},
title = {A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization},
booktitle = {International Conference on Artificial
Intelligence and Statistics (AISTATS)},
year = {2018},
titleurl = {laude-2018-proximal.pdf},
}
@article{laude2021lifting,
title = {Lifting the Convex Conjugate in Lagrangian Relaxations: {A} Tractable
Approach for Continuous Markov Random Fields},
author = {H Bauermeister and E Laude and T Möllenhoff and M Möller and D Cremers},
journal = {{SIAM} J. Imaging Sci.},
volume = {15},
number = {3},
pages = {1253--1281},
year = {2022},
keywords = {Markov random fields, moment relaxation, sum of squares, polynomial optimization, generalized conjugacy, optimal transport},
}
@inproceedings{laude-wu-cremers-aistats-19,
author = {E. Laude and T. Wu and D. Cremers},
title = {Optimization of Inf-Convolution Regularized Nonconvex Composite Problems},
booktitle = {International Conference on Artificial
Intelligence and Statistics (AISTATS)},
year = {2019},
titleurl = {laude-wu-cremers-aistats-19.pdf},
}
@inproceedings{mukkamala2021bregman,
address = {Cham},
author = {M. C. Mukkamala and F. Westerkamp and E. Laude and D. Cremers and P. Ochs},
booktitle = {Scale Space and Variational Methods in Computer Vision},
date-modified = {2021-05-18 17:23:47 +0200},
editor = {Elmoataz, Abderrahim and Fadili, Jalal and Qu{\'e}au, Yvain and Rabin, Julien and Simon, Lo{\"\i}c},
isbn = {978-3-030-75549-2},
pages = {204--215},
publisher = {Springer International Publishing},
title = {Bregman Proximal Gradient Algorithms for Deep Matrix Factorization},
year = {2021},
eprint = {1910.03638},
eprinttype = {arXiv},
eprintclass = {math.OC},
}
@inproceedings{demmel2020distributed,
author = {N Demmel and M Gao and E Laude and T Wu and D Cremers},
title = {Distributed Photometric Bundle Adjustment},
booktitle = {International Conference on 3D Vision (3DV)},
year = {2020},
award = {Oral Presentation},
keywords = {photometric-bundle-adjustment, slam, structure-from-motion, direct-method, distributed-optimization, mapping, splitting-method, penalty-method, loop-closure, odometry, consensus-optimization, dpba, vslam},
}