5 Publications
2026 | Conference Paper | LibreCat-ID: 67307 | 
Hotegni, S. S., & Peitz, S. (2026). SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion. 14th International Conference on Learning Representations (ICLR). https://doi.org/10.48550/arXiv.2509.21058
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2026 | Conference Paper | LibreCat-ID: 67300
Hotegni, S. S., & Peitz, S. (2026). Enhancing Adversarial Robustness Through Multi-objective Representation Learning. In W. Senn, M. Sanguineti, A. Saudargiene, I. V. Tetko, A. E. P. Villa, V. Jirsa, & Y. Bengio (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2025 (pp. 442–454). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-04558-4_35
LibreCat
| DOI
2025 | Journal Article | LibreCat-ID: 67316
Peitz, S., & Hotegni, S. S. (2025). Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art. Machine Learning with Applications, 100700. https://doi.org/10.1016/j.mlwa.2025.100700
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2024 | Conference Paper | LibreCat-ID: 46649 |
Hotegni, S. S., Berkemeier, M. B., & Peitz, S. (2024). Multi-Objective Optimization for Sparse Deep Multi-Task Learning. 2024 International Joint Conference on Neural Networks (IJCNN), 9. https://doi.org/10.1109/IJCNN60899.2024.10650994
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2023 | Conference Paper | LibreCat-ID: 45695 |
Hotegni, S. S., Mahabadi, S., & Vakilian, A. (n.d.). Approximation Algorithms for Fair Range Clustering. Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023. International Conference on Machine Learning, Honolulu, Hawaii, USA.
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5 Publications
2026 | Conference Paper | LibreCat-ID: 67307 |
Hotegni, S. S., & Peitz, S. (2026). SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion. 14th International Conference on Learning Representations (ICLR). https://doi.org/10.48550/arXiv.2509.21058
LibreCat
| DOI
| Download (ext.)
2026 | Conference Paper | LibreCat-ID: 67300
Hotegni, S. S., & Peitz, S. (2026). Enhancing Adversarial Robustness Through Multi-objective Representation Learning. In W. Senn, M. Sanguineti, A. Saudargiene, I. V. Tetko, A. E. P. Villa, V. Jirsa, & Y. Bengio (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2025 (pp. 442–454). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-04558-4_35
LibreCat
| DOI
2025 | Journal Article | LibreCat-ID: 67316
Peitz, S., & Hotegni, S. S. (2025). Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art. Machine Learning with Applications, 100700. https://doi.org/10.1016/j.mlwa.2025.100700
LibreCat
| DOI
2024 | Conference Paper | LibreCat-ID: 46649 |
Hotegni, S. S., Berkemeier, M. B., & Peitz, S. (2024). Multi-Objective Optimization for Sparse Deep Multi-Task Learning. 2024 International Joint Conference on Neural Networks (IJCNN), 9. https://doi.org/10.1109/IJCNN60899.2024.10650994
LibreCat
| DOI
| Download (ext.)
2023 | Conference Paper | LibreCat-ID: 45695 |
Hotegni, S. S., Mahabadi, S., & Vakilian, A. (n.d.). Approximation Algorithms for Fair Range Clustering. Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023. International Conference on Machine Learning, Honolulu, Hawaii, USA.
LibreCat
| Download (ext.)