@inproceedings{67308,
  abstract     = {{Optimizing large-scale multibody systems is a challenging task, particularly in the presence of multiple conflicting criteria. To prevent high simulation costs, surrogate models constructed from a small number of expensive model evaluations are very popular. However, it is difficult to ensure the optimality of the obtained solutions using a single pre-computed model. We present a back-and-forth approach between surrogate modeling and multi-objective optimization, and we compare different strategies for optimization, sampling, and surrogate modeling, to identify the most promising approach in terms of computational efficiency and solution quality.}},
  author       = {{Amakor, Augustina Chidinma and Berkemeier, Manuel B. and Wohlleben, Meike Claudia and Sextro, Walter 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         = {{978-3-032-04555-3}},
  keywords     = {{own, own-conference}},
  pages        = {{251–262}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Surrogate-Assisted Multi-objective Design of Complex Multibody Systems}}},
  doi          = {{10.1007/978-3-032-04555-3_21}},
  year         = {{2026}},
}

@inproceedings{67303,
  author       = {{Amakor, Augustina Chidinma and Sonntag, Konstantin and Peitz, Sebastian}},
  booktitle    = {{European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)}},
  keywords     = {{own, own-conference}},
  pages        = {{652--668}},
  title        = {{{Interactive Pareto navigation for deep multi-task learning}}},
  doi          = {{10.1007/978-3-032-37667-1_37}},
  year         = {{2026}},
}

@article{51159,
  abstract     = {{Sparsity is a highly desired feature in deep neural networks (DNNs) since it ensures numerical efficiency, improves the interpretability of models (due to the smaller number of relevant features), and robustness. In machine learning approaches based on linear models, it is well known that there exists a connecting path between the sparsest solution in terms of the $\ell^1$ norm,i.e., zero weights and the non-regularized solution, which is called the regularization path. Very recently, there was a first attempt to extend the concept of regularization paths to DNNs by means of treating the empirical loss and sparsity ($\ell^1$ norm) as two conflicting criteria and solving the resulting multiobjective optimization problem. However, due to the non-smoothness of the $\ell^1$ norm and the high number of parameters, this approach is not very efficient from a computational perspective. To overcome this limitation, we present an algorithm that allows for the approximation of the entire Pareto front for the above-mentioned objectives in a very efficient manner. We present numerical examples using both deterministic and stochastic gradients. We furthermore demonstrate that knowledge of the regularization path allows for a well-generalizing network parametrization.}},
  author       = {{Amakor, Augustina Chidinma and Sonntag, Konstantin and Peitz, Sebastian}},
  journal      = {{Machine Learning with Applications}},
  publisher    = {{Elsevier}},
  title        = {{{A multiobjective continuation method to compute the regularization path of deep neural networks}}},
  doi          = {{10.1016/j.mlwa.2025.100625}},
  volume       = {{19}},
  year         = {{2025}},
}