Published in ICLR 2026, Rio de Janeiro, Brazil , 2026
SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion Read more
Recommended citation: Hotegni, S.S., Peitz, S. (2026). SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion. In: 14th International Conference on Learning Representations, ICLR 2026. International Conference on Learning Representations, ICLR.
Published in ICANN 2025, Kaunas, Lithuania, 2025
Enhancing Adversarial Robustness through Multi-Objective Representation Learning Read more
Recommended citation: Hotegni, S.S., Peitz, S. (2026). Enhancing Adversarial Robustness Through Multi-objective Representation Learning. In: Senn, W., et al. Artificial Neural Networks and Machine Learning – ICANN 2025. ICANN 2025. Lecture Notes in Computer Science, vol 16068. Springer, Cham. https://doi.org/10.1007/978-3-032-04558-4_35
Published in Machine Learning with Applications, 2025
Multi-objective deep learning: Taxonomy and survey of the state of the art Read more
Recommended citation: Sebastian Peitz, Sèdjro Salomon Hotegni, Multi-objective deep learning: Taxonomy and survey of the state of the art, Machine Learning with Applications, Volume 21, 2025, 100700, ISSN 2666-8270, https://doi.org/10.1016/j.mlwa.2025.100700.
Published in IJCNN 2024, Yokohama, Japan, 2024
Multi-Objective Optimization for Sparse Deep Multi-Task Learning Read more
Recommended citation: S. S. Hotegni, M. Berkemeier and S. Peitz, "Multi-Objective Optimization for Sparse Deep Multi-Task Learning," 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024, pp. 1-9, doi: 10.1109/IJCNN60899.2024.10650994.
Published in ICML 2023, Hawaii, USA, 2023
Approximation Algorithms for Fair Range Clustering Read more
Recommended citation: Hotegni, S.S., Mahabadi, S. and Vakilian, A., 2023, July. Approximation Algorithms for Fair Range Clustering. In International Conference on Machine Learning (pp. 13270-13284). PMLR.