| Adversarial Robustness Toolbox v1. 0.0 MI Nicolae, M Sinn, MN Tran, B Buesser, A Rawat, M Wistuba, ... arXiv preprint arXiv:1807.01069, 2018 | 853 | 2018 |
| Learning time-series shapelets J Grabocka, N Schilling, M Wistuba, L Schmidt-Thieme Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 671 | 2014 |
| A survey on neural architecture search M Wistuba, A Rawat, T Pedapati arXiv preprint arXiv:1905.01392, 2019 | 461 | 2019 |
| Scalable gaussian process-based transfer surrogates for hyperparameter optimization M Wistuba, N Schilling, L Schmidt-Thieme Machine Learning 107 (1), 43-78, 2018 | 177 | 2018 |
| A comprehensive survey on hardware-aware neural architecture search H Benmeziane, KE Maghraoui, H Ouarnoughi, S Niar, M Wistuba, ... arXiv preprint arXiv:2101.09336, 2021 | 165 | 2021 |
| Learning hyperparameter optimization initializations M Wistuba, N Schilling, L Schmidt-Thieme 2015 IEEE international conference on data science and advanced analytics …, 2015 | 154 | 2015 |
| Few-shot Bayesian optimization with deep kernel surrogates M Wistuba, J Grabocka arXiv preprint arXiv:2101.07667, 2021 | 110 | 2021 |
| 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 | 110 | 2015 |
| Ultra-fast shapelets for time series classification M Wistuba, J Grabocka, L Schmidt-Thieme arXiv preprint arXiv:1503.05018, 2015 | 103 | 2015 |
| 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 | 97 | 2016 |
| 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 | 95 | 2016 |
| 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 | 90 | 2017 |
| Memory efficient continual learning with transformers B Ermis, G Zappella, M Wistuba, A Rawal, C Archambeau Advances in Neural Information Processing Systems 35, 10629-10642, 2022 | 89 | 2022 |
| Hardware-Aware Neural Architecture Search: Survey and Taxonomy. H Benmeziane, K El Maghraoui, H Ouarnoughi, S Niar, M Wistuba, ... IJCAI 2021, 4322-4329, 2021 | 85 | 2021 |
| 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 | 73 | 2018 |
| 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 | 70 | 2016 |
| Syne tune: A library for large scale hyperparameter tuning and reproducible research D Salinas, M Seeger, A Klein, V Perrone, M Wistuba, C Archambeau International Conference on Automated Machine Learning, 16/1-23, 2022 | 63 | 2022 |
| Sequential model-free hyperparameter tuning M Wistuba, N Schilling, L Schmidt-Thieme 2015 IEEE international conference on data mining, 1033-1038, 2015 | 63 | 2015 |
| 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 | 58 | 2019 |
| Practical Deep Learning Architecture Optimization M Wistuba 2018 IEEE 5th International Conference on Data Science and Advanced …, 2018 | 53* | 2018 |
Lars Schmidt-ThiemeUniversity of Hildesheim, GermanyVerified email at ismll.uni-hildesheim.de
