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Matthias Feurer
Matthias Feurer
Machine Learning group, University of Freiburg
Zweryfikowany adres z informatik.uni-freiburg.de - Strona główna
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
21022015
Hyperparameter Optimization
M Feurer, F Hutter
AutoML: Methods, Sytems, Challenges, 3-37, 2019
8992019
Initializing bayesian hyperparameter optimization via meta-learning
M Feurer, J Springenberg, F Hutter
Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015
508*2015
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
3872013
Towards Automatically-Tuned Deep Neural Networks
H Mendoza, A Klein, M Feurer, JT Springenberg, M Urban, M Burkart, ...
Automated Machine Learning, 135-149, 2019
290*2019
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
170*2022
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
143*2022
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
106*2021
Practical Transfer Learning for Bayesian Optimization
M Feurer, B Letham, F Hutter, E Bakshy
arXiv:1802.02219v3, 2022
105*2022
Practical Automated Machine Learning for the AutoML Challenge 2018
M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter
ICML 2018 AutoML Workshop, 2018
862018
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
502021
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
332019
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
282021
Towards Further Automation in AutoML
M Feurer, F Hutter
ICML 2018 AutoML Workshop, 2018
262018
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
132019
Squirrel: A Switching Hyperparameter Optimizer
N Awad, G Shala, D Deng, N Mallik, M Feurer, K Eggensperger, ...
arXiv preprint arXiv:2012.08180, 2020
32020
OpenML: a networked science platform for machine learning
J Vanschoren, JN van Rijn, B Bischl, G Casalicchio, M Lang, M Feurer
ICML 2015 MLOSS Workshop 3, 2015
32015
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
H Weerts, F Pfisterer, M Feurer, K Eggensperger, E Bergman, N Awad, ...
arXiv:2303.08485, 2023
2023
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives
M Feurer, K Eggensperger, E Bergman, F Pfisterer, B Bischl, F Hutter
arXiv:2212.04183 [cs.LG], 2022
2022
Bayesian Optimization with a Neural Network Meta-learned on Synthetic Data Only
S Müller, S Pineda Arango, M Feurer, J Grabocka, F Hutter
Workshop on Meta-Learning at the Conference on Neural Information Processing …, 2022
2022
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