% generated by bibtexbrowser % % Encoding: UTF-8 @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}, }