| Adversarial Robustness Toolbox v1. 0.0 MI Nicolae, M Sinn, MN Tran, B Buesser, A Rawat, M Wistuba, ... arXiv preprint arXiv:1807.01069, 2018 | 707 | 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 | 608 | 2014 |
| A survey on neural architecture search M Wistuba, A Rawat, T Pedapati arXiv preprint arXiv:1905.01392, 2019 | 400 | 2019 |
| Scalable gaussian process-based transfer surrogates for hyperparameter optimization M Wistuba, N Schilling, L Schmidt-Thieme Machine Learning 107 (1), 43-78, 2018 | 151 | 2018 |
| Learning hyperparameter optimization initializations M Wistuba, N Schilling, L Schmidt-Thieme 2015 IEEE international conference on data science and advanced analytics …, 2015 | 135 | 2015 |
| 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 | 113 | 2021 |
| Ultra-fast shapelets for time series classification M Wistuba, J Grabocka, L Schmidt-Thieme arXiv preprint arXiv:1503.05018, 2015 | 100 | 2015 |
| Hyperparameter search space pruning–a new component for sequential model-based hyperparameter optimization M Wistuba, N Schilling, L Schmidt-Thieme Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015 | 91 | 2015 |
| Fast classification of univariate and multivariate time series through shapelet discovery J Grabocka, M Wistuba, L Schmidt-Thieme Knowledge and information systems 49, 429-454, 2016 | 90 | 2016 |
| Two-stage transfer surrogate model for automatic hyperparameter optimization M Wistuba, N Schilling, L Schmidt-Thieme Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016 | 87 | 2016 |
| Personalized deep learning for tag recommendation HTH Nguyen, M Wistuba, J Grabocka, LR Drumond, L Schmidt-Thieme Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia …, 2017 | 85 | 2017 |
| Few-shot bayesian optimization with deep kernel surrogates M Wistuba, J Grabocka arXiv preprint arXiv:2101.07667, 2021 | 84 | 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 | 69 | 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 | 63 | 2016 |
| Hardware-Aware Neural Architecture Search: Survey and Taxonomy. H Benmeziane, K El Maghraoui, H Ouarnoughi, S Niar, M Wistuba, ... IJCAI, 4322-4329, 2021 | 60 | 2021 |
| 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 | 57 | 2022 |
| Sequential model-free hyperparameter tuning M Wistuba, N Schilling, L Schmidt-Thieme 2015 IEEE international conference on data mining, 1033-1038, 2015 | 57 | 2015 |
| Hyperparameter optimization with factorized multilayer perceptrons N Schilling, M Wistuba, L Drumond, L Schmidt-Thieme Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015 | 51 | 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 | 49 | 2019 |
| Hyperparameter optimization machines M Wistuba, N Schilling, L Schmidt-Thieme 2016 IEEE international conference on data science and advanced analytics …, 2016 | 48 | 2016 |
Lars Schmidt-ThiemeUniversity of Hildesheim, Germany在 ismll.uni-hildesheim.de 的电子邮件经过验证