M. Haas, D. Holzmüller, U. von Luxburg, and I. Steinwart. NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems, page 20763-20826. Association for Computing Machinery, (2024)
S. Tovey, C. Lohrmann, and C. Holm. Dataset, (2024)Related to: Tovey, Samuel James and Lohrmann, Christoph and Holm, Christian, Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning, Machine Learning: Science and Technology, 2024. doi: 10.1088/2632-2153/ad5f73.
D. Holzmüller, L. Grinsztajn, and I. Steinwart. Software, (2024)Related to: David Holzmüller, Léo Grinsztajn, and Ingo Steinwart. Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data, 2024. arXiv: 2407.04491.
A. Stein, and A. Barth. Monte Carlo and Quasi-Monte Carlo Methods (MCQMC 2018), volume 324 of Springer Proceedings in Mathematics & Statistics, page 445-466. Springer, (2020)
A. Bordignon, E. Cances, G. Dusson, G. Kemlin, R. Lainez Reyes, and B. Stamm. Software, (2024)Related to: A. Bordignon, E. Cancès, G. Dusson, G.Kemlin, R.A. Lainez Reyes, B. Stamm, Fully guaranteed and computable error bounds on the energy for periodic Kohn-Sham equations with convex density functionals, preprint.