André Biedenkapp
Cited by
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Smac v3: Algorithm configuration in python
M Lindauer, K Eggensperger, M Feurer, S Falkner, A Biedenkapp, ...
URL https://github. com/automl/SMAC3, 2017
Efficient parameter importance analysis via ablation with surrogates
A Biedenkapp, M Lindauer, K Eggensperger, F Hutter, C Fawcett, H Hoos
Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017
CAVE: Configuration Assessment, Visualization and Evaluation
A Biedenkapp, J Marben, M Lindauer, F Hutter
LION12, 2018
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
Dynamic algorithm configuration: foundation of a new meta-algorithmic framework
A Biedenkapp, HF Bozkurt, T Eimer, F Hutter, M Lindauer
Proceedings of the Twenty-fourth European Conference on Artificial …, 2020
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
On the importance of hyperparameter optimization for model-based reinforcement learning
B Zhang, R Rajan, L Pineda, N Lambert, A Biedenkapp, K Chua, F Hutter, ...
International Conference on Artificial Intelligence and Statistics, 4015-4023, 2021
Towards TempoRL: Learning When to Act
A Biedenkapp, R Rajan, F Hutter, M Lindauer
Workshop on Inductive Biases, Invariances and Generalization in …, 2020
Towards white-box benchmarks for algorithm control
A Biedenkapp, HF Bozkurt, F Hutter, M Lindauer
arXiv preprint arXiv:1906.07644, 2019
Sample-Efficient Automated Deep Reinforcement Learning
JKH Franke, G Köhler, A Biedenkapp, F Hutter
arXiv preprint arXiv:2009.01555, 2020
In-Loop Meta-Learning with Gradient-Alignment Reward
S Müller, A Biedenkapp, F Hutter
arXiv preprint arXiv:2102.03275, 2021
Squirrel: A Switching Hyperparameter Optimizer
N Awad, G Shala, D Deng, N Mallik, M Feurer, K Eggensperger, ...
arXiv preprint arXiv:2012.08180, 2020
Learning Step-Size Adaptation in CMA-ES
G Shala*, A Biedenkapp*, N Awad, S Adriaensen, M Lindauer, F Hutter
International Conference on Parallel Problem Solving from Nature, 691-706, 2020
Learning Heuristic Selection with Dynamic Algorithm Configuration
D Speck*, A Biedenkapp*, F Hutter, R Mattmüller, M Lindauer
arXiv preprint arXiv:2006.08246, 2020
MDP Playground: Controlling Dimensions of Hardness in Reinforcement Learning
R Rajan, JLB Diaz, S Guttikonda, F Ferreira, A Biedenkapp, F Hutter
arXiv preprint arXiv:1909.07750, 2019
Towards Self-Paced Context Evaluation for Contextual Reinforcement Learning
T Eimer, A Biedenkapp, F Hutter, M Lindauer
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