| Validity of time reversal for testing Granger causality I Winkler, D Panknin, D Bartz, KR Müller, S Haufe IEEE Transactions on Signal Processing 64 (11), 2746-2760, 2016 | 68 | 2016 |
| Fast cross-validation via sequential testing. T Krueger, D Panknin, ML Braun J. Mach. Learn. Res. 16 (1), 1103-1155, 2015 | 54 | 2015 |
| Higher order stationary subspace analysis D Panknin, P Von Bünau, M Kawanabe, FC Meinecke, KR Müller Journal of Physics: Conference Series 699 (1), 012021, 2016 | 5 | 2016 |
| Optimal sampling density for nonparametric regression D Panknin, KR Müller, S Nakajima arXiv preprint arXiv:2105.11990, 2021 | 4 | 2021 |
| Sharing hash codes for multiple purposes W Pronobis, D Panknin, J Kirschnick, V Srinivasan, W Samek, V Markl, ... Japanese Journal of Statistics and Data Science 1, 215-246, 2018 | 4 | 2018 |
| Fast cross-validation via sequential analysis T Krueger, D Panknin, M Braun Neural Information Processing Systems (NIPS), Big Learning Workshop, 2011 | 3 | 2011 |
| Local Bandwidth Estimation via Mixture of Gaussian Processes D Panknin, S Nakajima, TB Bui, KR Müller arXiv 2019, 2019 | 2 | 2019 |
| Detecting Changes in Wind Turbine Sensory Data D Panknin, T Krueger, M Braun, K Mueller, S Duell Procs. NIPS 2013 Workshop: Machine Learining for Sustainability, 1-5, 2013 | 2 | 2013 |
| Local Function Complexity for Active Learning via Mixture of Gaussian Processes D Panknin, S Chmiela, KR Müller, S Nakajima arXiv preprint arXiv:1902.10664, 2019 | 1 | 2019 |
| Towards model-agnostic active learning in regression via identification of problem-intrinsic properties D Panknin | | 2023 |
| ALICE III—Autonomes Lernen in Komplexen Umgebungen III A von Beuningen, V Brandstetter, P von Bünau, S Depeweg, R Glauert, ... | | 2022 |
| Compressed Sensing and Machine Learning D Panknin TU Berlin, 2012 | | 2012 |
| Appendix to Fast Cross-Validation via Sequential Analysis T Krueger, D Panknin, M Braun | | |
