| Learning time-series shapelets J Grabocka, N Schilling, M Wistuba, L Schmidt-Thieme Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 291 | 2014 |
| A survey on neural architecture search M Wistuba, A Rawat, T Pedapati arXiv preprint arXiv:1905.01392, 2019 | 92 | 2019 |
| Adversarial Robustness Toolbox v1. 0.0 MI Nicolae, M Sinn, MN Tran, B Buesser, A Rawat, M Wistuba, ... arXiv preprint arXiv:1807.01069, 2018 | 85 | 2018 |
| Ultra-fast shapelets for time series classification M Wistuba, J Grabocka, L Schmidt-Thieme arXiv preprint arXiv:1503.05018, 2015 | 63 | 2015 |
| Learning hyperparameter optimization initializations M Wistuba, N Schilling, L Schmidt-Thieme 2015 IEEE international conference on data science and advanced analytics …, 2015 | 50 | 2015 |
| Scalable gaussian process-based transfer surrogates for hyperparameter optimization M Wistuba, N Schilling, L Schmidt-Thieme Machine Learning 107 (1), 43-78, 2018 | 47 | 2018 |
| Adversarial Robustness Toolbox v0. 2.2 MI Nicolae, M Sinn, TN Minh, A Rawat, M Wistuba, V Zantedeschi, ... | 46 | 2018 |
| Personalized deep learning for tag recommendation HTH Nguyen, M Wistuba, J Grabocka, LR Drumond, L Schmidt-Thieme Pacific-Asia Conference on Knowledge Discovery and Data Mining, 186-197, 2017 | 40 | 2017 |
| Fast classification of univariate and multivariate time series through shapelet discovery J Grabocka, M Wistuba, L Schmidt-Thieme Knowledge and information systems 49 (2), 429-454, 2016 | 39 | 2016 |
| Learning DTW-shapelets for time-series classification M Shah, J Grabocka, N Schilling, M Wistuba, L Schmidt-Thieme Proceedings of the 3rd IKDD Conference on Data Science, 2016, 1-8, 2016 | 32 | 2016 |
| Hyperparameter search space pruning–a new component for sequential model-based hyperparameter optimization M Wistuba, N Schilling, L Schmidt-Thieme Joint European Conference on Machine Learning and Knowledge Discovery in …, 2015 | 32 | 2015 |
| Hyperparameter optimization with factorized multilayer perceptrons N Schilling, M Wistuba, L Drumond, L Schmidt-Thieme Joint European Conference on Machine Learning and Knowledge Discovery in …, 2015 | 30 | 2015 |
| Personalized tag recommendation for images using deep transfer learning HTH Nguyen, M Wistuba, L Schmidt-Thieme Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017 | 25 | 2017 |
| Practical Deep Learning Architecture Optimization M Wistuba 2018 IEEE 5th International Conference on Data Science and Advanced …, 2018 | 24* | 2018 |
| Deep learning architecture search by neuro-cell-based evolution with function-preserving mutations M Wistuba Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018 | 24 | 2018 |
| Adversarial phenomenon in the eyes of Bayesian deep learning A Rawat, M Wistuba, MI Nicolae arXiv preprint arXiv:1711.08244, 2017 | 23 | 2017 |
| Automatic Frankensteining: Creating complex ensembles autonomously M Wistuba, N Schilling, L Schmidt-Thieme Proceedings of the 2017 SIAM International Conference on Data Mining, 741-749, 2017 | 23 | 2017 |
| Optimal exploitation of clustering and history information in multi-armed bandit D Bouneffouf, S Parthasarathy, H Samulowitz, M Wistub arXiv preprint arXiv:1906.03979, 2019 | 21 | 2019 |
| Two-stage transfer surrogate model for automatic hyperparameter optimization M Wistuba, N Schilling, L Schmidt-Thieme Joint European conference on machine learning and knowledge discovery in …, 2016 | 20 | 2016 |
| Scalable discovery of time-series shapelets J Grabocka, M Wistuba, L Schmidt-Thieme arXiv preprint arXiv:1503.03238, 2015 | 19 | 2015 |
Lars Schmidt-ThiemeUniversity of Hildesheim, GermanyVerified email at ismll.uni-hildesheim.de
