André Biedenkapp
Title
Cited by
Cited by
Year
Smac v3: Algorithm configuration in python
M Lindauer, K Eggensperger, M Feurer, S Falkner, A Biedenkapp, ...
URL https://github. com/automl/SMAC3, 2017
54*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
272017
Dynamic algorithm configuration: foundation of a new meta-algorithmic framework
A Biedenkapp, HF Bozkurt, T Eimer, F Hutter, M Lindauer
ECAI 2020, 427-434, 2020
202020
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
162019
CAVE: Configuration Assessment, Visualization and Evaluation
A Biedenkapp, J Marben, M Lindauer, F Hutter
LION12, 2018
152018
Sample-efficient automated deep reinforcement learning
JKH Franke, G Köhler, A Biedenkapp, F Hutter
arXiv preprint arXiv:2009.01555, 2020
92020
Learning Heuristic Selection with Dynamic Algorithm Configuration
D Speck*, A Biedenkapp*, F Hutter, R Mattmüller, M Lindauer
arXiv preprint arXiv:2006.08246, 2020
62020
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
62019
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
52021
Towards TempoRL: Learning When to Act
A Biedenkapp, R Rajan, F Hutter, M Lindauer
Workshop on Inductive Biases, Invariances and Generalization in …, 2020
52020
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization
S Izquierdo, J Guerrero-Viu, S Hauns, G Miotto, S Schrodi, A Biedenkapp, ...
8th ICML Workshop on Automated Machine Learning (AutoML), 2021
3*2021
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
32020
DACBench: A Benchmark Library for Dynamic Algorithm Configuration
T Eimer, A Biedenkapp, M Reimer, S Adriaensen, F Hutter, M Lindauer
arXiv preprint arXiv:2105.08541, 2021
22021
Towards white-box benchmarks for algorithm control
A Biedenkapp, HF Bozkurt, F Hutter, M Lindauer
arXiv preprint arXiv:1906.07644, 2019
22019
Self-Paced Context Evaluation for Contextual Reinforcement Learning
T Eimer, A Biedenkapp, F Hutter, M Lindauer
arXiv preprint arXiv:2106.05110, 2021
12021
MDP Playground: A Design and Debug Testbed for Reinforcement Learning
R Rajan, JLB Diaz, S Guttikonda, F Ferreira, A Biedenkapp, JO von Hartz, ...
1*2021
In-Loop Meta-Learning with Gradient-Alignment Reward
S Müller, A Biedenkapp, F Hutter
arXiv preprint arXiv:2102.03275, 2021
12021
Squirrel: A Switching Hyperparameter Optimizer
N Awad, G Shala, D Deng, N Mallik, M Feurer, K Eggensperger, ...
arXiv preprint arXiv:2012.08180, 2020
12020
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
C Benjamins, T Eimer, F Schubert, A Biedenkapp, B Rosenhahn, F Hutter, ...
arXiv preprint arXiv:2110.02102, 2021
2021
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
M Lindauer, K Eggensperger, M Feurer, A Biedenkapp, D Deng, ...
arXiv preprint arXiv:2109.09831, 2021
2021
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