Publications
Project publications
- Kseniya Akhalaya, Franziska Nestler, and Daniel Potts. Fast and interpretable Support Vector Classification based on the truncated ANOVA decomposition. arXiv: 2402.02438, 2024.
[ bib | arXiv ]
- Theresa Wagner, John W. Pearson, and Martin Stoll. A Preconditioned Interior Point Method for Support Vector Machines Using an ANOVA-Decomposition and NFFT-Based Matrix-Vector Products. arXiv: 2312.00538, 2023.
[ bib | arXiv ]
- Felix Bartel, Lutz Kämmerer, Daniel Potts, and Tino Ullrich. On the reconstruction of functions from values at subsampled quadrature points. arXiv: 2208.13597, 2022.
[ bib | arXiv ]
- Fatima Antarou Ba, and Michael Quellmalz. Accelerating the Sinkhorn algorithm for sparse multi-marginal optimal transport by fast Fourier transforms. arXiv: 2208.03120, 2022.
[ bib | arXiv ]
- Laura Lippert, and Daniel Potts. Variable Transformations in combination with Wavelets and ANOVA for high-dimensional approximation. arXiv: 2207.12826, 2022.
[ bib | arXiv ]
- Franziska Nestler, Martin Stoll, and Theresa Wagner. Learning in high-dimensional feature spaces using ANOVA-based fast matrix-vector multiplication. Foundations of Data Science, 4, 423-440, 2022.
[ bib | doi ]
- Daniel Potts, and Michael Schmischke. Interpretable transformed ANOVA approximation on the example of the prevention of forest fires. arXiv: 2110.07353, 2021.
[ bib | arXiv ]
- Johannes Hertrich, Fatima Antarou Ba, and Gabriele Steidl. Sparse Mixture Models Inspired by ANOVA Decompositions. Electron. Trans. Numer. Anal., 55, 142-168, 2022.
[ bib ]
- Kai Bergermann, and Martin Stoll. Matrix function-based centrality measures for layer-coupled mulitplex networks. ArXiv e-prints, 2021.
[ bib | arXiv ]
- Dominik Alfke, Miriam Gondos, Lucile Peroche, and Martin Stoll. A Study of Graph-Based Approaches for Semi-Supervised Time Series Classification. ArXiv e-prints, 2021.
[ bib | arXiv ]
- Potts, D., and Schmischke, M.. Interpretable Approximation of High-Dimensional Data. SIAM J. Math. Data Sci., 2021, accepted.
[ bib ]
Theses
- Michael Schmischke. Dissertation: Interpretable Approximation of High-Dimensional Data based on the ANOVA Decomposition. Universitätsverlag Chemnitz, 2022.
[ bib ]
- Jeremias Piljug. Bachelorarbeit: Hoch-Dimensionale ANOVA Approximation in Anwendungen. TU Chemnitz, Fakultät für Mathematik, Professur für Angewandte Funktionalanalyis (Prof. D. Potts, M. Schmischke), 2021.
[ bib ]
Previous related papers (preliminary work)
- Laura Lippert, Daniel Potts, and Tino Ullrich. Fast Hyperbolic Wavelet Regression meets ANOVA. ArXiv e-prints, 2021.
[ bib | arXiv ]
- Felix Bartel, Michael Schmischke, and Daniel Potts. Grouped Transformations and Regularization in High-Dimensional Explainable ANOVA Approximation. SIAM Journal on Scientific Computing, 2021 (accepted).
[ bib | arXiv ]
- Theresa Wagner. Fast Matrix-Vector Multiplication for the ANOVA Kernel. Chemnitz University of Technology, 2020.
[ bib | pdf ]
- Potts, D., and Schmischke, M.. Learning multivariate functions with low-dimensional structures using polynomial bases. J. Comput. App. Math., 2021, accepted.
[ bib ]