André Biedenkapp
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SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.
M Lindauer, K Eggensperger, M Feurer, A Biedenkapp, D Deng, ...
J. Mach. Learn. Res. 23 (54), 1-9, 2022
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 (AISTATS …, 2021
Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework
A Biedenkapp, HF Bozkurt, T Eimer, F Hutter, M Lindauer
European Conference on Artificial Intelligence (ECAI'20) 24, 427-434, 2020
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), 773-779, 2017
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
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
J Parker-Holder, R Rajan, X Song, A Biedenkapp, Y Miao, T Eimer, ...
Journal of Artificial Intelligence Research 74, 517-568, 2022
CAVE: Configuration Assessment, Visualization and Evaluation
A Biedenkapp, J Marben, M Lindauer, F Hutter
Learning and Intelligent Optimization (LION'18) 12, 115-130, 2018
Sample-Efficient Automated Deep Reinforcement Learning
JKH Franke, G Köhler, A Biedenkapp, F Hutter
International Conference on Learning Representations (ICLR'21) 9, 2021
TempoRL: Learning When to Act
A Biedenkapp, R Rajan, F Hutter, M Lindauer
International Conference on Machine Learning (ICML'21) 38, 914-924, 2021
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
Learning Heuristic Selection with Dynamic Algorithm Configuration
D Speck*, A Biedenkapp*, F Hutter, R Mattmüller, M Lindauer
International Conference on Automated Planning and Scheduling (ICAPS'21) 31 …, 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 (PPSN'20 …, 2020
Self-Paced Context Evaluation for Contextual Reinforcement Learning
T Eimer, A Biedenkapp, F Hutter, M Lindauer
International Conference on Machine Learning (ICML'21), 2948-2958, 2021
DACBench: A Benchmark Library for Dynamic Algorithm Configuration
T Eimer, A Biedenkapp, M Reimer, S Adriaensen, F Hutter, M Lindauer
International Joint Conference on Artificial Intelligence (IJCAI'21) 30 …, 2021
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters
M Lindauer, M Feurer, K Eggensperger, A Biedenkapp, F Hutter
Data Science Meets Optimisation Workshop (DSO@IJCAI'19), 2019
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
C Benjamins, T Eimer, F Schubert, A Biedenkapp, B Rosenhahn, F Hutter, ...
Workshop on Ecological Theory of Reinforcement Learning (EcoRL@NeurIPS'21), 2021
Towards White-Box Benchmarks for Algorithm Control
A Biedenkapp, HF Bozkurt, F Hutter, M Lindauer
Data Science Meets Optimisation Workshop (DSO@IJCAI'19), 2019
Automated Dynamic Algorithm Configuration
S Adriaensen, A Biedenkapp, G Shala, N Awad, T Eimer, M Lindauer, ...
Journal of Artificial Intelligence Research 75, 1633-1699, 2022
Squirrel: A Switching Hyperparameter Optimizer
N Awad, G Shala, D Deng, N Mallik, M Feurer, K Eggensperger, ...
NeurIPS 2020 BBO challenge, 2020
MDP playground: A design and debug testbed for reinforcement learning
R Rajan, JLB Diaz, S Guttikonda, F Ferreira, A Biedenkapp, JO von Hartz, ...
arXiv preprint arXiv:1909.07750, 2019
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