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 | 2954 | 2015 |
Deep learning with convolutional neural networks for EEG decoding and visualization RT Schirrmeister, JT Springenberg, LDJ Fiederer, M Glasstetter, ... Human brain mapping, 2017 | 2893 | 2017 |
Auto-sklearn 2.0: Hands-free automl via meta-learning M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter The Journal of Machine Learning Research 23 (1), 11936-11996, 2022 | 471* | 2022 |
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 | 469 | 2013 |
SMAC3: A versatile Bayesian optimization package for hyperparameter optimization M Lindauer, K Eggensperger, M Feurer, A Biedenkapp, D Deng, ... The Journal of Machine Learning Research 23 (1), 2475-2483, 2022 | 410* | 2022 |
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second N Hollmann, S Müller, K Eggensperger, F Hutter International Conference on Learning Representations (ICLR'23), 2023 | 230 | 2023 |
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 | 162 | 2015 |
Practical Automated Machine Learning for the AutoML Challenge 2018 M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter ICML 2018 AutoML Workshop, 2018 | 115 | 2018 |
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 | 97 | 2021 |
Pitfalls and Best Practices in Algorithm Configuration K Eggensperger, M Lindauer, F Hutter Journal of Artificial Intelligence Research (JAIR) 64, 861-893, 2019 | 73 | 2019 |
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 | 64 | 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 | 58 | 2017 |
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 | 52 | 2019 |
Neural Networks for Predicting Algorithm Runtime Distributions K Eggensperger, M Lindauer, F Hutter Proceedings of the International Joint Conference on Artificial Intelligence …, 2018 | 31 | 2018 |
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 | 22 | 2019 |
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 | 20 | 2016 |
Surrogate Benchmarks for Hyperparameter Optimization. K Eggensperger, F Hutter, HH Hoos, K Leyton-Brown MetaSel@ ECAI, 24-31, 2014 | 20 | 2014 |
Can fairness be automated? Guidelines and opportunities for fairness-aware AutoML H Weerts, F Pfisterer, M Feurer, K Eggensperger, E Bergman, N Awad, ... Journal of Artificial Intelligence Research 79, 639-677, 2024 | 15 | 2024 |
Mind the gap: Measuring generalization performance across multiple objectives M Feurer, K Eggensperger, E Bergman, F Pfisterer, B Bischl, F Hutter International Symposium on Intelligent Data Analysis, 130-142, 2023 | 6 | 2023 |
Neural Model-based Optimization with Right-Censored Observations K Eggensperger, K Haase, P Müller, M Lindauer, F Hutter arXiv preprint arXiv:2009.13828, 2020 | 6 | 2020 |