Sublabel-Accurate Relaxation of Nonconvex Energies. - Thomas Möllenhoff; Emanuel Laude; Michael Möller 001; Jan Lellmann; Daniel Cremers (CVPR, 2016)

Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies. - Emanuel Laude; Thomas Möllenhoff; Michael Möller 001; Jan Lellmann; Daniel Cremers (ECCV (1), 2016)

Discrete-Continuous Splitting for Weakly Supervised Learning. - Emanuel Laude; Jan-Hendrik Lange; Frank R. Schmidt; Bjoern Andres; Daniel Cremers (CoRR, 2017)

A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization. - Emanuel Laude; Tao Wu 006; Daniel Cremers (AISTATS, 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 (CVPR, 2018)

Optimization of Inf-Convolution Regularized Nonconvex Composite Problems. - Emanuel Laude; Tao Wu 006; Daniel Cremers (AISTATS, 2019)

Bregman Proximal Framework for Deep Linear Neural Networks. - Mahesh Chandra Mukkamala; Felix Westerkamp; Emanuel Laude; Daniel Cremers; Peter Ochs (CoRR, 2019)

Distributed Photometric Bundle Adjustment. - Nikolaus Demmel; Maolin Gao; Emanuel Laude; Tao Wu 006; Daniel Cremers (3DV, 2020)

Bregman Proximal Mappings and Bregman-Moreau Envelopes Under Relative Prox-Regularity. - Emanuel Laude; Peter Ochs; Daniel Cremers (J. Optim. Theory Appl., 2020)

Bregman Proximal Gradient Algorithms for Deep Matrix Factorization. - Mahesh Chandra Mukkamala; Felix Westerkamp; Emanuel Laude; Daniel Cremers; Peter Ochs (SSVM, 2021)

Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields. - Hartmut Bauermeister; Emanuel Laude; Thomas Möllenhoff; Michael Möller 001; Daniel Cremers (SIAM J. Imaging Sci., 2022)

Dualities for Non-Euclidean Smoothness and Strong Convexity under the Light of Generalized Conjugacy. - Emanuel Laude; Andreas Themelis; Panagiotis Patrinos (SIAM J. Optim., 2023)

Convex Relaxations for Manifold-Valued Markov Random Fields with Approximation Guarantees. - Robin Kenis; Emanuel Laude; Panagiotis Patrinos (ECCV (87), 2024)

Adaptive Proximal Gradient Methods Are Universal Without Approximation. - Konstantinos A. Oikonomidis; Emanuel Laude; Puya Latafat; Andreas Themelis; Panagiotis Patrinos (ICML, 2024)

The inexact power augmented Lagrangian method for constrained nonconvex optimization. - Alexander Bodard; Konstantinos A. Oikonomidis; Emanuel Laude; Panagiotis Patrinos (CoRR, 2024)

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 008 (CoRR, 2025)