Katharina Eggensperger
Cited by
Cited by
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
Deep learning with convolutional neural networks for EEG decoding and visualization
RT Schirrmeister, JT Springenberg, LDJ Fiederer, M Glasstetter, ...
Human brain mapping, 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
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
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), 1-9, 2022
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
Practical Automated Machine Learning for the AutoML Challenge 2018
M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter
ICML 2018 AutoML Workshop, 2018
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
Pitfalls and Best Practices in Algorithm Configuration
K Eggensperger, M Lindauer, F Hutter
Journal of Artificial Intelligence Research (JAIR) 64, 861-893, 2019
Efficient Parameter Importance Analysis via Ablation with Surrogates
A Biedenkapp, M Lindauer, K Eggensperger, F Hutter, C Fawcett, ...
Proceedings of the AAAI conference, 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
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
Neural Networks for Predicting Algorithm Runtime Distributions
K Eggensperger, M Lindauer, F Hutter
Proceedings of the International Joint Conference on Artificial Intelligence …, 2018
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
N Hollmann, S Müller, K Eggensperger, F Hutter
arXiv preprint arXiv:2207.01848, 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
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
Neural Model-based Optimization with Right-Censored Observations
K Eggensperger, K Haase, P Müller, M Lindauer, F Hutter
arXiv preprint arXiv:2009.13828, 2020
Squirrel: A Switching Hyperparameter Optimizer
N Awad, G Shala, D Deng, N Mallik, M Feurer, K Eggensperger, ...
arXiv preprint arXiv:2012.08180, 2020
Filtering Outliers in Bayesian Optimization
R Martinez-Cantin, K Tee, M McCourt, K Eggensperger
Hyperparameter Optimization for Machine Learning Problems in BCI
A Meinel, K Eggensperger, M Tangermann, F Hutter
Proceedings of the 6th International Brain-Computer Interface Meeting: BCI …, 2016
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