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Katharina Eggensperger
Katharina Eggensperger
Early Career Group Leader "AutoML for Science" | University of Tübingen
Verified email at uni-tuebingen.de - Homepage
Title
Cited by
Cited by
Year
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
27982015
Deep learning with convolutional neural networks for EEG decoding and visualization
RT Schirrmeister, JT Springenberg, LDJ Fiederer, M Glasstetter, ...
Human brain mapping, 2017
26402017
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
4592013
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
404*2022
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
354*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
1562023
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
1522015
Practical Automated Machine Learning for the AutoML Challenge 2018
M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter
ICML 2018 AutoML Workshop, 2018
1092018
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
852021
Pitfalls and Best Practices in Algorithm Configuration
K Eggensperger, M Lindauer, F Hutter
Journal of Artificial Intelligence Research (JAIR) 64, 861-893, 2019
702019
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
622018
Efficient Parameter Importance Analysis via Ablation with Surrogates
A Biedenkapp, M Lindauer, K Eggensperger, F Hutter, C Fawcett, ...
Proceedings of the AAAI conference, 2017
562017
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
462019
Neural Networks for Predicting Algorithm Runtime Distributions
K Eggensperger, M Lindauer, F Hutter
Proceedings of the International Joint Conference on Artificial Intelligence …, 2018
312018
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
222019
Surrogate Benchmarks for Hyperparameter Optimization.
K Eggensperger, F Hutter, HH Hoos, K Leyton-Brown
MetaSel@ ECAI, 24-31, 2014
192014
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
182016
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
102024
Neural Model-based Optimization with Right-Censored Observations
K Eggensperger, K Haase, P Müller, M Lindauer, F Hutter
arXiv preprint arXiv:2009.13828, 2020
62020
Squirrel: a switching hyperparameter optimizer
N Awad, G Shala, D Deng, N Mallik, M Feurer, K Eggensperger, ...
arXiv preprint arXiv:2012.08180, 2020
52020
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