Hertrich, Christoph

Number of items: 7.
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  • Bertschinger, Daniel, Hertrich, Christoph, Jungeblut, Paul, Miltzow, Tillmann, Weber, Simon (2024). Training fully connected neural networks is ∃R-complete. In Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (Eds.), NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems (pp. 36222 - 36237). Curran Associates, Inc..
  • Cole, Richard, Hertrich, Christoph, Tao, Yixin, Végh, László A. (2024). A first order method for linear programming parameterized by circuit imbalance. In Vygen, Jens, Byrka, Jarosław (Eds.), Integer Programming and Combinatorial Optimization - 25th International Conference, IPCO 2024, Proceedings (pp. 57 - 70). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-59835-7_5
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  • Cole, Richard, Hertrich, Christoph, Tao, Yixin, Vegh, Laszlo A. (2025). A first order method for linear programming parameterized by circuit imbalance. Mathematical Programming, https://doi.org/10.1007/s10107-025-02264-7 picture_as_pdf
  • Hertrich, Christoph, Basu, Amitabh, Summa, Marco D.I., Skutella, Martin (2023). Towards lower bounds on the depth of Relu neural networks. SIAM Journal on Discrete Mathematics, 37(2), 997-1029. https://doi.org/10.1137/22M1489332 picture_as_pdf
  • Hertrich, Christoph, Skutella, Martin (2023). Provably good solutions to the knapsack problem via neural networks of bounded size. Informs Journal on Computing, 35(5), 1079 - 1097. https://doi.org/10.1287/ijoc.2021.0225 picture_as_pdf
  • Hertrich, Christoph, Sering, Leon (2023). ReLU neural networks of polynomial size for exact maximum flow computation. In Del Pia, Alberto, Kaibel, Volker (Eds.), Integer Programming and Combinatorial Optimization - 24th International Conference, IPCO 2023, Proceedings (pp. 187-202). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-32726-1_14 picture_as_pdf
  • Froese, Vincent, Hertrich, Christoph, Niedermeier, Rolf (2022). The computational complexity of ReLU network training parameterized by data dimensionality. Journal of Artificial Intelligence Research, 74, 1775-1790. https://doi.org/10.1613/JAIR.1.13547 picture_as_pdf