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ORCIDEmanuel Laude
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2020 – today
- 2024
[c8]Konstantinos A. Oikonomidis, Emanuel Laude, Puya Latafat, Andreas Themelis, Panagiotis Patrinos:
Adaptive Proximal Gradient Methods Are Universal Without Approximation. ICML 2024- [i7]Konstantinos A. Oikonomidis, Emanuel Laude, Puya Latafat, Andreas Themelis, Panagiotis Patrinos:
Adaptive proximal gradient methods are universal without approximation. CoRR abs/2402.06271 (2024) - 2023
- [j3]Emanuel Laude
, Andreas Themelis, Panagiotis Patrinos:
Dualities for Non-Euclidean Smoothness and Strong Convexity under the Light of Generalized Conjugacy. SIAM J. Optim. 33(4): 2721-2749 (2023) - 2022
- [j2]Hartmut Bauermeister, Emanuel Laude, Thomas Möllenhoff, Michael Möller, Daniel Cremers:
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields. SIAM J. Imaging Sci. 15(3): 1253-1281 (2022) - 2021
- [c7]Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers, Peter Ochs:
Bregman Proximal Gradient Algorithms for Deep Matrix Factorization. SSVM 2021: 204-215 - [i6]Hartmut Bauermeister, Emanuel Laude, Thomas Möllenhoff, Michael Möller, Daniel Cremers:
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields. CoRR abs/2107.06028 (2021) - 2020
- [j1]Emanuel Laude, Peter Ochs, Daniel Cremers:
Bregman Proximal Mappings and Bregman-Moreau Envelopes Under Relative Prox-Regularity. J. Optim. Theory Appl. 184(3): 724-761 (2020) - [c6]Nikolaus Demmel, Maolin Gao, Emanuel Laude, Tao Wu, Daniel Cremers:
Distributed Photometric Bundle Adjustment. 3DV 2020: 140-149
2010 – 2019
- 2019
- [c5]Emanuel Laude, Tao Wu, Daniel Cremers:
Optimization of Inf-Convolution Regularized Nonconvex Composite Problems. AISTATS 2019: 547-556 - [i5]Emanuel Laude, Tao Wu, Daniel Cremers:
Optimization of Inf-Convolution Regularized Nonconvex Composite Problems. CoRR abs/1903.11690 (2019) - [i4]Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers, Peter Ochs:
Bregman Proximal Framework for Deep Linear Neural Networks. CoRR abs/1910.03638 (2019) - 2018
- [c4]Emanuel Laude, Tao Wu, Daniel Cremers:
A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization. AISTATS 2018: 491-499 - [c3]Emanuel Laude, Jan-Hendrik Lange, Jonas Schüpfer, Csaba Domokos, Laura Leal-Taixé, Frank R. Schmidt, Bjoern Andres, Daniel Cremers:
Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs. CVPR 2018: 1614-1624 - 2017
- [i3]Emanuel Laude, Jan-Hendrik Lange, Frank R. Schmidt, Bjoern Andres, Daniel Cremers:
Discrete-Continuous Splitting for Weakly Supervised Learning. CoRR abs/1705.05020 (2017) - 2016
- [c2]Thomas Möllenhoff, Emanuel Laude, Michael Möller, Jan Lellmann, Daniel Cremers:
Sublabel-Accurate Relaxation of Nonconvex Energies. CVPR 2016: 3948-3956 - [c1]Emanuel Laude, Thomas Möllenhoff, Michael Möller, Jan Lellmann, Daniel Cremers:
Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies. ECCV (1) 2016: 614-627 - [i2]Emanuel Laude, Thomas Möllenhoff, Michael Möller, Jan Lellmann, Daniel Cremers:
Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies. CoRR abs/1604.01980 (2016) - 2015
- [i1]Thomas Möllenhoff, Emanuel Laude, Michael Möller, Jan Lellmann, Daniel Cremers:
Sublabel-Accurate Relaxation of Nonconvex Energies. CoRR abs/1512.01383 (2015)
Coauthor Index
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