Froese, V., Hertrich, C., and Niedermeier, R. The computational complexity of relu network training parameterized by data dimensionality. J. Artif. Intell. Res., 74:1775-1790, 2022. doi: 10.1613/jair.1.13547. URL https://doi.org/10.1613/jair.1.13547.
Ganian, R. and Korchemna, V. The complexity of bayesian network learning: Revisiting the superstructure. In Ranzato, M., Beygelzimer, A., Dauphin, Y. N., Liang, P., and Vaughan, J. W. (eds.), Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pp. 430-442, 2021. URL https://proceedings.neurips.cc/paper/2021/bit/040a99f23e8960763e680041c601acab-Abstract.html.
Ganian, R. and Ordyniak, S. Solving integer linear programs by exploiting variable-constraint interactions: A survey. Algorithms, 12(12):248, 2019. doi: 10.3390/a12120248. URL https://doi.org/10.3390/a12120248.
Ganian, R., Kanj, I. A., Ordyniak, S., and Szeider, S. Parameterized algorithms for the matrix completion problem. In Dy, J. G. and Krause, A. (eds.), Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholm, Sweden, July 10-15, 2018, volume 80 of Proceedings of Machine Learning Research, pp. 1642-1651. PMLR, 2018. URL http://proceedings.mlr.press/v80/ganian18a.html.
Ganian, R., Hamm, T., Korchemna, V., Okrasa, K., and Simonov, K. The complexity of k-means clustering when little is known. In Chaudhuri, K., Jegelka, S., Song, L., Szepesvári, C., Niu, G., and Sabato, S. (eds.), International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, volume 162 of Proceedings of Machine Learning Research, pp. 6960-6987. PMLR, 2022. URL https://proceedings.mlr.press/v162/ganian22a.html.
Gao, H., Cai, L., and Ji, S. Adaptive convolutional relus. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pp. 3914-3921. AAAI Press, 2020. URL https://ojs.aaai.org/index.php/AAAI/article/view/5805.
Goel, S., Klivans, A. R., Manurangsi, P., and Reichman, D. Tight hardness results for training depth-2 relu networks. In Lee, J. R. (ed.), 12th Innovations in Theoretical Computer Science Conference, ITCS 2021, January 6-8, 2021, Virtual Conference, volume 185 of LIPIcs, pp. 22:1-22:14. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021. doi: 10.4230/LIPics.ITCS.2021.22. URL https://doi.org/10.4230/LIPics.ITCS.2021.22.
Goodfellow, I., Bengio, Y., and Courville, A. Deep Learning. MIT Press, 2016. URL http://www.deeplearningbook.org/.
Grittemeier, N. and Komusiewicz, C. Learning bayesian networks under sparsity constraints: A parameterized complexity analysis. J. Artif. Intell. Res., 74:1225-1267, 2022. doi: 10.1613/jair.1.13138. URL https://doi.org/10.1613/jair.1.13138.
Haase, C., Hertrich, C., and Loho, G. Lower bounds on the depth of integral relu neural networks via lattice polytopes. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. URL https://openreview.net/pdf?id=2mvAL0AWaxY.
Harding, E. F. The number of partitions of a set of  $n$  points in  $k$  dimensions induced by hyperplanes. Proceedings of the Edinburgh mathematical society, 15(4):285-289, 1967.
Heggernes, P. and Telle, J. A. Partitioning graphs into generalized dominating sets. Nordic J. of Computing, 5(2):128-142, jun 1998. ISSN 1236-6064.
Hertrich, C. Facets of neural network complexity. PhD thesis, Technical University of Berlin, Germany, 2022. URL https://nbn-resolving.org/urn:nbn:de:101:1-2022033001583912451077.