D. Holzmüller, V. Zaverkin, J. Kästner, und I. Steinwart. Software, (2023)Related to: David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2023. arXiv: 2203.09410.
V. Zaverkin, D. Holzmüller, L. Bonfirraro, und J. Kästner. Dataset, (2023)Related to: Viktor Zaverkin, David Holzmüller, Luca Bonfirraro, Johannes Kästner. Transfer learning for chemically accurate interatomic neural network potentials, Phys. Chem. Chem. Phys., 2023, 25, 5383-5396. doi: 10.1039/D2CP05793J.
D. Holzmüller, V. Zaverkin, J. Kästner, und I. Steinwart. Software, (2022)Related to: David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2022. arXiv: 2203.09410.
M. Nonnenmacher, D. Reeb, und I. Steinwart. Machine Learning and Knowledge Discovery in Databases : Research Track, Volume 3 von Lecture Notes in Computer Science, Seite 87-102. Berlin, Springer, (2021)
D. Holzmüller, und I. Steinwart. Software, (2022)Related to: David Holzmüller and Ingo Steinwart. Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent, 2020. arXiv: 2002.04861.
D. Holzmüller, V. Zaverkin, J. Kästner, und I. Steinwart. Software, (2022)Related to: David, Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2022. arXiv: 2203.09410.
D. Holzmüller. Software, (2022)Related to: David Holzmüller and Dirk Pflüger. Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework. Sparse Grids and Applications - Munich 2018 (2021). doi: 10.1007/978-3-030-81362-8_4.
D. Holzmüller. Software, (2021)Related to: David Holzmüller. On the Universality of the Double Descent Peak in Ridgeless Regression. International Conference on Learning Representations, 2021. arXiv: 2010.01851.
V. Zaverkin, D. Holzmüller, I. Steinwart, und J. Kästner. Software, (2021)Related to: V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,” J. Chem. Theory Comput. 17, 6658–6670 (2021). doi: 10.1021/acs.jctc.1c00527.
M. Hansmann, M. Kohler, und H. Walk. Annals of the Institute of Statistical Mathematics, (2019)Correction to: https://doi.org/10.1007/s10463-018-0674-9.
P. Thomann, I. Blaschzyk, M. Meister, und I. Steinwart. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 54, Seite 1329-1337. Red Hook, NY, Curran, (2017)
A. Christmann, und I. Steinwart. Advances in neural information processing systems 23 : 24th Annual Conference on Neural Information Processing Systems 2010, 1, Seite 406-414. Red Hook, NY, Curran, (2011)