| Efficient and robust automated machine learning M Feurer, A Klein, K Eggensperger, J Springenberg, M Blum, F Hutter Advances in Neural Information Processing Systems, 2962-2970, 2015 | 1770 | 2015 |
| Deep learning with convolutional neural networks for EEG decoding and visualization RT Schirrmeister, JT Springenberg, LDJ Fiederer, M Glasstetter, ... Human brain mapping, 2017 | 1343 | 2017 |
| Towards an empirical foundation for assessing Bayesian optimization of hyperparameters K Eggensperger, M Feurer, F Hutter, J Bergstra, J Snoek, H Hoos, ... NeurIPS workshop on Bayesian Optimization in Theory and Practice 10, 2013 | 342 | 2013 |
| Efficient benchmarking of hyperparameter optimizers via surrogates K Eggensperger, F Hutter, HH Hoos, K Leyton-brown Proceedings of the 29th AAAI Conference on Artificial Intelligence, 1114-1120, 2015 | 116* | 2015 |
| Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter arXiv preprint arXiv:2007.04074 [cs.LG], 2021 | 82* | 2021 |
| Practical Automated Machine Learning for the AutoML Challenge 2018 M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter ICML 2018 AutoML Workshop, 2018 | 70 | 2018 |
| Smac v3: Algorithm Configuration in Python M Lindauer, K Eggensperger, M Feurer, S Falkner, A Biedenkapp, ... 2017, 2017 | 54 | 2017 |
| Efficient Benchmarking of Algorithm Configurators via Model-based Surrogates K Eggensperger, M Lindauer, HH Hoos, F Hutter, K Leyton-Brown Machine Learning 101 (1), 15-41, 2018 | 52 | 2018 |
| Efficient Parameter Importance Analysis via Ablation with Surrogates A Biedenkapp, M Lindauer, K Eggensperger, F Hutter, C Fawcett, ... Proceedings of the AAAI conference, 2017 | 39 | 2017 |
| Pitfalls and Best Practices in Algorithm Configuration K Eggensperger, M Lindauer, F Hutter Journal of Artificial Intelligence Research (JAIR) 64, 861-893, 2019 | 36 | 2019 |
| BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters M Lindauer, K Eggensperger, M Feurer, A Biedenkapp, J Marben, ... arXiv preprint arXiv:1908.06756, 2019 | 25 | 2019 |
| Neural Networks for Predicting Algorithm Runtime Distributions K Eggensperger, M Lindauer, F Hutter Proceedings of the International Joint Conference on Artificial Intelligence …, 2018 | 20* | 2018 |
| SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. M Lindauer, K Eggensperger, M Feurer, A Biedenkapp, D Deng, ... J. Mach. Learn. Res. 23, 54:1-54:9, 2022 | 14 | 2022 |
| Automatic Bone Parameter Estimation for Skeleton Tracking in Optical Motion Capture T Schubert, K Eggensperger, A Gkogkidis, F Hutter, T Ball, W Burgard Proceedings of the IEEE International Conference on Robotics and Automation …, 2016 | 13 | 2016 |
| HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO K Eggensperger, P Müller, N Mallik, M Feurer, R Sass, A Klein, N Awad, ... Neural Information Processing Systems Track on Datasets and Benchmarks …, 2021 | 11 | 2021 |
| Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters M Lindauer, M Feurer, K Eggensperger, A Biedenkapp, F Hutter arXiv preprint arXiv:1908.06674, 2019 | 9 | 2019 |
| Neural Model-based Optimization with Right-Censored Observations K Eggensperger, K Haase, P Müller, M Lindauer, F Hutter arXiv preprint arXiv:2009.13828, 2020 | 3 | 2020 |
| Squirrel: A Switching Hyperparameter Optimizer N Awad, G Shala, D Deng, N Mallik, M Feurer, K Eggensperger, ... arXiv preprint arXiv:2012.08180, 2020 | 2 | 2020 |
| Designing and Understanding Convolutional Networks for Decoding Executed Movements from EEG RT Schirrmeister, LDJ Fiederer, JT Springenberg, M Glasstetter, ... The First Biannual Neuroadaptive Technology Conference, 143, 2017 | 2 | 2017 |
| Filtering Outliers in Bayesian Optimization R Martinez-Cantin, K Tee, M McCourt, K Eggensperger | 1 | 2017 |
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyПотвърден имейл адрес: cs.uni-freiburg.de
