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ORCIDMoritz Herrmann
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2020 – today
- 2024
[j5]Moritz Herrmann
, Daniyal Kazempour, Fabian Scheipl, Peer Kröger:
Enhancing cluster analysis via topological manifold learning. Data Min. Knowl. Discov. 38(3): 840-887 (2024)- [c1]Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl:
Position: Why We Must Rethink Empirical Research in Machine Learning. ICML 2024 - [i7]Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl:
Position: Why We Must Rethink Empirical Research in Machine Learning. CoRR abs/2405.02200 (2024) - 2023
- [j4]Moritz Herrmann, Florian Pfisterer, Fabian Scheipl:
A geometric framework for outlier detection in high-dimensional data. WIREs Data. Mining. Knowl. Discov. 13(3) (2023) - [i6]Jana Gauss, Fabian Scheipl, Moritz Herrmann:
DCSI - An improved measure of cluster separability based on separation and connectedness. CoRR abs/2310.12806 (2023) - 2022
- [b1]Moritz Herrmann:
Towards more reliable machine learning: conceptual insights and practical approaches for unsupervised manifold learning and supervised benchmark studies. Ludwig Maximilian University of Munich, Germany, 2022 - [j3]Christina Nießl, Moritz Herrmann, Chiara Wiedemann, Giuseppe Casalicchio, Anne-Laure Boulesteix:
Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results. WIREs Data Mining Knowl. Discov. 12(2) (2022) - [i5]Moritz Herrmann, Florian Pfisterer, Fabian Scheipl:
A geometric framework for outlier detection in high-dimensional data. CoRR abs/2207.00367 (2022) - [i4]Moritz Herrmann, Daniyal Kazempour, Fabian Scheipl, Peer Kröger:
Enhancing cluster analysis via topological manifold learning. CoRR abs/2207.00510 (2022) - 2021
- [j2]Moritz Herrmann, Philipp Probst, Roman Hornung, Vindi Jurinovic, Anne-Laure Boulesteix:
Large-scale benchmark study of survival prediction methods using multi-omics data. Briefings Bioinform. 22(3) (2021) - [i3]Moritz Herrmann, Fabian Scheipl:
A geometric perspective on functional outlier detection. CoRR abs/2109.06849 (2021) - 2020
- [i2]Moritz Herrmann, Philipp Probst, Roman Hornung, Vindi Jurinovic, Anne-Laure Boulesteix:
Large-scale benchmark study of survival prediction methods using multi-omics data. CoRR abs/2003.03621 (2020) - [i1]Moritz Herrmann, Fabian Scheipl:
Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction. CoRR abs/2012.11987 (2020)
2010 – 2019
- 2014
- [j1]Joshua Wing Kei Ho, Youngsook L. Jung, Tao Liu, Burak Han Alver, Soohyun Lee, Kohta Ikegami, Kyung-Ah Sohn, Aki Minoda, Michael Y. Tolstorukov, Alex Appert, Stephen C. J. Parker, Tingting Gu, Anshul Kundaje, Nicole C. Riddle, Eric Bishop, Thea A. Egelhofer, Sheng'en Shawn Hu, Artyom A. Alekseyenko, Andreas Rechtsteiner, Dalal Asker, Jason A. Belsky, Sarah K. Bowman, Q. Brent Chen, Ron A.-J. Chen, Daniel S. Day, Yan Dong, Andrea C. Dose, Xikun Duan, Charles B. Epstein, Sevinc Ercan, Elise A. Feingold, Francesco Ferrari, Jacob M. Garrigues, Nils Gehlenborg, Peter J. Good, Psalm Haseley, Daniel He, Moritz Herrmann, Michael M. Hoffman, Tess E. Jeffers, Peter V. Kharchenko, Paulina Kolasinska-Zwierz, Chitra V. Kotwaliwale, Nischay Kumar, Sasha A. Langley, Erica Larschan, Isabel Latorre, Maxwell W. Libbrecht, Xueqiu Lin, Richard Park, Michael J. Pazin, Hoang N. Pham, Annette Plachetka, Bo Qin, Yuri B. Schwartz, Noam Shoresh, Przemyslaw Stempor, Anne Vielle, Chengyang Wang, Christina M. Whittle, Huiling Xue, Robert E. Kingston, Ju Han Kim, Bradley E. Bernstein, Abby F. Dernburg, Vincenzo Pirrotta, Mitzi I. Kuroda, William S. Noble, Thomas D. Tullius, Manolis Kellis, David M. MacAlpine, Susan Strome, Sarah C. R. Elgin, Xiaole Shirley Liu, Jason D. Lieb, Julie Ahringer, Gary H. Karpen, Peter J. Park:
Comparative analysis of metazoan chromatin organization Open. Nat. 512(7515): 449-452 (2014)
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