| Approximation of high-dimensional periodic functions with Fourier-based methods D Potts, M Schmischke SIAM Journal on Numerical Analysis 59 (5), 2393-2429, 2021 | 40 | 2021 |
| Learning multivariate functions with low-dimensional structures using polynomial bases D Potts, M Schmischke Journal of Computational and Applied Mathematics 403, 113821, 2022 | 20 | 2022 |
| Interpretable Approximation of High-Dimensional Data D Potts, M Schmischke SIAM Journal on Mathematics of Data Science 3 (4), 1301–1323, 2021 | 19 | 2021 |
| Grouped transformations and regularization in high-dimensional explainable ANOVA approximation F Bartel, D Potts, M Schmischke SIAM Journal on Scientific Computing 44 (3), A1606-A1631, 2022 | 18 | 2022 |
| Interpretable Transformed ANOVA Approximation on the Example of the Prevention of Forest Fires D Potts, M Schmischke Frontiers in Applied Mathematics and Statistics 8, 795250, 2022 | 6 | 2022 |
| Interpretable approximation of high-dimensional data based on the ANOVA decomposition MSM Schmischke | 6 | 2022 |
| Nonequispaced fast Fourier transform (NFFT) interface for Julia M Schmischke arXiv preprint arXiv:1810.09891, 2018 | 1 | 2018 |
| Interpretable ANOVA Approximation of High-Dimensional Scattered Data D Potts, M Schmischke SIAM J. Numer. Anal 57 (2), 547-562, 2019 | | 2019 |
| Schnelle Faltung mit dem Sinc-Kern M Schmischke | | 2017 |
| Explainable Approximation in High Dimensions: Fourier-Based Algorithms Meet Kernel Methods F Nestler, D Potts, M Schmischke, M Stoll, T Wagner | | |
| High-Dimensional Explainable ANOVA Approximation M Schmischke | | |
| A Fourier approach to learning sparse additive models M Schmischke | | |
Daniel PottsChemnitz University of TechnologyVerified email at mathematik.tu-chemnitz.de
