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Matthias Feurer
Tytuł
Cytowane przez
Cytowane przez
Rok
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
29772015
Hyperparameter Optimization
M Feurer, F Hutter
AutoML: Methods, Sytems, Challenges, 3-37, 2019
18472019
Initializing bayesian hyperparameter optimization via meta-learning
M Feurer, J Springenberg, F Hutter
Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015
674*2015
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter
Journal of Machine Learning Research 23 (261), 1-61, 2022
640*2022
Towards an empirical foundation for assessing bayesian optimization of hyperparameters
K Eggensperger, M Feurer, F Hutter, J Bergstra, J Snoek, H Hoos, ...
NIPS workshop on Bayesian Optimization in Theory and Practice, 1-5, 2013
4772013
SMAC3: A versatile Bayesian optimization package for hyperparameter optimization
M Lindauer, K Eggensperger, M Feurer, A Biedenkapp, D Deng, ...
Journal of Machine Learning Research 23 (54), 1-9, 2022
451*2022
Towards Automatically-Tuned Deep Neural Networks
H Mendoza, A Klein, M Feurer, JT Springenberg, M Urban, M Burkart, ...
Automated Machine Learning, 135-149, 2019
373*2019
OpenML Benchmarking Suites
B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ...
Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021
234*2021
Practical Transfer Learning for Bayesian Optimization
M Feurer, B Letham, F Hutter, E Bakshy
arXiv:1802.02219v3, 2022
175*2022
OpenML-Python: an extensible Python API for OpenML
M Feurer, JN van Rijn, A Kadra, P Gijsbers, N Mallik, S Ravi, A Müller, ...
Journal of Machine Learning Research 22 (100), 1-5, 2021
1112021
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 1, 2021
1082021
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
532019
PFNs4BO: In-Context Learning for Bayesian Optimization
S Müller, M Feurer, N Hollmann, F Hutter
40th International Conference on Machine Learning, 2023
322023
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
232024
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
232019
OpenML-CTR23–A curated tabular regression benchmarking suite
SF Fischer, M Feurer, B Bischl
AutoML Conference 2023 (Workshop), 2023
212023
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
72023
Position: Why We Must Rethink Empirical Research in Machine Learning
M Herrmann, FJD Lange, K Eggensperger, G Casalicchio, M Wever, ...
Forty-first International Conference on Machine Learning, 2024
5*2024
Squirrel: A Switching Hyperparameter Optimizer
N Awad, G Shala, D Deng, N Mallik, M Feurer, K Eggensperger, ...
arXiv preprint arXiv:2012.08180, 2020
52020
Interpretable machine learning for TabPFN
D Rundel, J Kobialka, C von Crailsheim, M Feurer, T Nagler, D Rügamer
World Conference on Explainable Artificial Intelligence, 465-476, 2024
42024
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