@inproceedings{67307,
  author       = {{Hotegni, Sedjro Salomon and Peitz, Sebastian}},
  booktitle    = {{14th International Conference on Learning Representations (ICLR)}},
  keywords     = {{own, own-conference}},
  title        = {{{SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion}}},
  doi          = {{10.48550/arXiv.2509.21058}},
  year         = {{2026}},
}

@inproceedings{67300,
  abstract     = {{Deep neural networks (DNNs) are vulnerable to small adversarial perturbations, which are tiny changes to the input data that appear insignificant but cause the model to produce drastically different outputs. Many defense methods require modifying model architectures during evaluation or performing test-time data purification. This not only introduces additional complexity but is often architecture-dependent. We show, however, that robust feature learning during training can significantly enhance DNN robustness. We propose MOREL, a multi-objective approach that aligns natural and adversarial features using cosine similarity and multi-positive contrastive losses to encourage similar features for same-class inputs. Extensive experiments demonstrate that MOREL significantly improves robustness against both white-box and black-box attacks. Our code is available at https://github.com/salomonhotegni/MOREL.}},
  author       = {{Hotegni, Sedjro Salomon and Peitz, Sebastian}},
  booktitle    = {{Artificial Neural Networks and Machine Learning – ICANN 2025}},
  editor       = {{Senn, Walter and Sanguineti, Marcello and Saudargiene, Ausra and Tetko, Igor V. and Villa, Alessandro E. P. and Jirsa, Viktor and Bengio, Yoshua}},
  isbn         = {{978-3-032-04558-4}},
  keywords     = {{own, own-conference}},
  pages        = {{442–454}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Enhancing Adversarial Robustness Through Multi-objective Representation Learning}}},
  doi          = {{10.1007/978-3-032-04558-4_35}},
  year         = {{2026}},
}

@article{67316,
  author       = {{Peitz, Sebastian and Hotegni, Sedjro Salomon}},
  journal      = {{Machine Learning with Applications}},
  keywords     = {{own, own-journal}},
  number       = {{100700}},
  title        = {{{Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art}}},
  doi          = {{10.1016/j.mlwa.2025.100700}},
  year         = {{2025}},
}

@inproceedings{46649,
  abstract     = {{Different conflicting optimization criteria arise naturally in various Deep
Learning scenarios. These can address different main tasks (i.e., in the
setting of Multi-Task Learning), but also main and secondary tasks such as loss
minimization versus sparsity. The usual approach is a simple weighting of the
criteria, which formally only works in the convex setting. In this paper, we
present a Multi-Objective Optimization algorithm using a modified Weighted
Chebyshev scalarization for training Deep Neural Networks (DNNs) with respect
to several tasks. By employing this scalarization technique, the algorithm can
identify all optimal solutions of the original problem while reducing its
complexity to a sequence of single-objective problems. The simplified problems
are then solved using an Augmented Lagrangian method, enabling the use of
popular optimization techniques such as Adam and Stochastic Gradient Descent,
while efficaciously handling constraints. Our work aims to address the
(economical and also ecological) sustainability issue of DNN models, with a
particular focus on Deep Multi-Task models, which are typically designed with a
very large number of weights to perform equally well on multiple tasks. Through
experiments conducted on two Machine Learning datasets, we demonstrate the
possibility of adaptively sparsifying the model during training without
significantly impacting its performance, if we are willing to apply
task-specific adaptations to the network weights. Code is available at
https://github.com/salomonhotegni/MDMTN.}},
  author       = {{Hotegni, Sedjro Salomon and Berkemeier, Manuel Bastian and Peitz, Sebastian}},
  booktitle    = {{2024 International Joint Conference on Neural Networks (IJCNN)}},
  issn         = {{ 2161-4407}},
  location     = {{Yokohama, Japan}},
  pages        = {{9}},
  publisher    = {{IEEE}},
  title        = {{{Multi-Objective Optimization for Sparse Deep Multi-Task Learning}}},
  doi          = {{10.1109/IJCNN60899.2024.10650994}},
  year         = {{2024}},
}

@inproceedings{45695,
  author       = {{Hotegni, Sedjro Salomon and Mahabadi, Sepideh and Vakilian, Ali}},
  booktitle    = {{Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023.}},
  keywords     = {{Fair range clustering}},
  location     = {{Honolulu, Hawaii, USA}},
  title        = {{{Approximation Algorithms for Fair Range Clustering}}},
  year         = {{2023}},
}