Emanuel Laude

According to our database, Emanuel Laude authored at least 16 papers between 2016 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

ConStellaration: A dataset of QI-like stellarator plasma boundaries and optimization benchmarks.

Santiago A. Cadena, Andrea Merlo, Emanuel Laude, Alexander Bauer, Atul Agrawal, Maria Pascu, Marija Savtchouk, Enrico Guiraud, Lukas Bonauer, Stuart Hudson, Markus Kaiser

CoRR, June, 2025

2024

The inexact power augmented Lagrangian method for constrained nonconvex optimization.

Alexander Bodard, Konstantinos A. Oikonomidis, Emanuel Laude, Panagiotis Patrinos

CoRR, 2024

Adaptive Proximal Gradient Methods Are Universal Without Approximation.

Konstantinos A. Oikonomidis, Emanuel Laude, Puya Latafat, Andreas Themelis, Panagiotis Patrinos

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Convex Relaxations for Manifold-Valued Markov Random Fields with Approximation Guarantees.

Robin Kenis, Emanuel Laude, Panagiotis Patrinos

Proceedings of the Computer Vision - ECCV 2024, 2024

2023

Dualities for Non-Euclidean Smoothness and Strong Convexity under the Light of Generalized Conjugacy.

Emanuel Laude, Andreas Themelis, Panagiotis Patrinos

SIAM J. Optim., December, 2023

2022

Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields.

Hartmut Bauermeister, Emanuel Laude, Thomas Möllenhoff, Michael Möller, Daniel Cremers

SIAM J. Imaging Sci., September, 2022

2021

Bregman Proximal Gradient Algorithms for Deep Matrix Factorization.

Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers, Peter Ochs

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2021

2020

Bregman Proximal Mappings and Bregman-Moreau Envelopes Under Relative Prox-Regularity.

Emanuel Laude, Peter Ochs, Daniel Cremers

J. Optim. Theory Appl., 2020

Distributed Photometric Bundle Adjustment.

Nikolaus Demmel, Maolin Gao, Emanuel Laude, Tao Wu, Daniel Cremers

Proceedings of the 8th International Conference on 3D Vision, 2020

2019

Bregman Proximal Framework for Deep Linear Neural Networks.

Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers, Peter Ochs

CoRR, 2019

Optimization of Inf-Convolution Regularized Nonconvex Composite Problems.

Emanuel Laude, Tao Wu, Daniel Cremers

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs.

Emanuel Laude, Jan-Hendrik Lange, Jonas Schüpfer, Csaba Domokos, Laura Leal-Taixé, Frank R. Schmidt, Bjoern Andres, Daniel Cremers

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization.

Emanuel Laude, Tao Wu, Daniel Cremers

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Discrete-Continuous Splitting for Weakly Supervised Learning.

Emanuel Laude, Jan-Hendrik Lange, Frank R. Schmidt, Bjoern Andres, Daniel Cremers

CoRR, 2017

2016

Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies.

Emanuel Laude, Thomas Möllenhoff, Michael Möller, Jan Lellmann, Daniel Cremers

Proceedings of the Computer Vision - ECCV 2016, 2016

Sublabel-Accurate Relaxation of Nonconvex Energies.

Thomas Möllenhoff, Emanuel Laude, Michael Möller, Jan Lellmann, Daniel Cremers

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016