| Deep one-class classification L Ruff, R Vandermeulen, N Goernitz, L Deecke, SA Siddiqui, A Binder, ... International conference on machine learning, 4393-4402, 2018 | 3410 | 2018 |
| A unifying review of deep and shallow anomaly detection L Ruff, JR Kauffmann, RA Vandermeulen, G Montavon, W Samek, M Kloft, ... Proceedings of the IEEE 109 (5), 756-795, 2021 | 1401 | 2021 |
| Deep semi-supervised anomaly detection L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ... International conference on learning representations (ICLR), 2020 | 966 | 2020 |
| Toward supervised anomaly detection N Görnitz, M Kloft, K Rieck, U Brefeld Journal of Artificial Intelligence Research 46, 235-262, 2013 | 607 | 2013 |
| Multiple kernel k-means with incomplete kernels X Liu, X Zhu, M Li, L Wang, E Zhu, T Liu, M Kloft, D Shen, J Yin, W Gao IEEE transactions on pattern analysis and machine intelligence 42 (5), 1191-1204, 2020 | 518 | 2020 |
| lp-Norm Multiple Kernel Learning M Kloft, U Brefeld, S Sonnenburg, A Zien The Journal of Machine Learning Research 12, 953-997, 2011 | 494 | 2011 |
| Predicting MOOC dropout over weeks using machine learning methods M Kloft, F Stiehler, Z Zheng, N Pinkwart Proceedings of the EMNLP 2014 workshop on analysis of large scale social …, 2014 | 432 | 2014 |
| Efficient and accurate lp-norm multiple kernel learning M Kloft, U Brefeld, P Laskov, KR Müller, A Zien, S Sonnenburg Advances in neural information processing systems 22, 2009 | 365 | 2009 |
| Efficient and effective regularized incomplete multi-view clustering X Liu, M Li, C Tang, J Xia, J Xiong, L Liu, M Kloft, E Zhu IEEE transactions on pattern analysis and machine intelligence, 2021 | 356 | 2021 |
| Explainable deep one-class classification P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, M Kloft, KR Müller International Conference on Learning Representations (ICLR), 2021 | 347 | 2021 |
| Image anomaly detection with generative adversarial networks L Deecke, R Vandermeulen, L Ruff, S Mandt, M Kloft Joint european conference on machine learning and knowledge discovery in …, 2018 | 337 | 2018 |
| Cloze test helps: Effective video anomaly detection via learning to complete video events G Yu, S Wang, Z Cai, E Zhu, C Xu, J Yin, M Kloft Proceedings of the 28th ACM international conference on multimedia, 583-591, 2020 | 270 | 2020 |
| Machine learning in chemical engineering: A perspective AM Schweidtmann, E Esche, A Fischer, M Kloft, JU Repke, S Sager, ... Chemie Ingenieur Technik 93 (12), 2029-2039, 2021 | 266 | 2021 |
| Neural transformation learning for deep anomaly detection beyond images C Qiu, T Pfrommer, M Kloft, S Mandt, M Rudolph International conference on machine learning, 8703-8714, 2021 | 226 | 2021 |
| Online anomaly detection under adversarial impact M Kloft, P Laskov Proceedings of the thirteenth international conference on artificial …, 2010 | 215 | 2010 |
| Multiview subspace clustering via co-training robust data representation J Liu, X Liu, Y Yang, X Guo, M Kloft, L He IEEE Transactions on Neural Networks and Learning Systems 33 (10), 5177-5189, 2021 | 157 | 2021 |
| Effective end-to-end unsupervised outlier detection via inlier priority of discriminative network S Wang, Y Zeng, X Liu, E Zhu, J Yin, C Xu, M Kloft Advances in neural information processing systems 32, 2019 | 147 | 2019 |
| Non-sparse multiple kernel learning M Kloft, U Brefeld, P Laskov, S Sonnenburg NIPS Workshop on Kernel Learning: Automatic Selection of Optimal Kernels 4, 5, 2008 | 135 | 2008 |
| Rethinking assumptions in deep anomaly detection L Ruff, RA Vandermeulen, BJ Franks, KR Müller, M Kloft arXiv preprint arXiv:2006.00339, 2020 | 133 | 2020 |
| Mixed kernel based extreme learning machine for electric load forecasting Y Chen, M Kloft, Y Yang, C Li, L Li Neurocomputing 312, 90-106, 2018 | 122 | 2018 |
Klaus-Robert MüllerTU Berlin & Korea University & Google DeepMind & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
