| SMAC3: A versatile Bayesian optimization package for hyperparameter optimization M Lindauer, K Eggensperger, M Feurer, A Biedenkapp, D Deng, ... The Journal of Machine Learning Research 23 (1), 2475-2483, 2022 | 660* | 2022 |
| 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 | 169 | 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 | 166 | 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 | 109 | 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 | 74 | 2017 |
| SMAC v3: Algorithm Configuration in Python M Lindauer, K Eggensperger, M Feurer, S Falkner, A Biedenkapp, ... URL https://github. com/automl/SMAC3, 2017 | 73* | 2017 |
| Contextualize Me--The Case for Context in Reinforcement Learning C Benjamins, T Eimer, F Schubert, A Mohan, S Döhler, A Biedenkapp, ... Transactions on Machine Learning Research, 2023 | 71 | 2023 |
| 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 | 70 | 2022 |
| 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 | 68 | 2019 |
| Sample-Efficient Automated Deep Reinforcement Learning JKH Franke, G Köhler, A Biedenkapp, F Hutter International Conference on Learning Representations (ICLR'21) 9, 2021 | 67 | 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 | 65* | 2021 |
| CAVE: Configuration Assessment, Visualization and Evaluation A Biedenkapp, J Marben, M Lindauer, F Hutter Learning and Intelligent Optimization (LION'18) 12, 115-130, 2018 | 57 | 2018 |
| 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 | 53 | 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 | 47 | 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 | 43 | 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 | 42* | 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 | 39 | 2021 |
| 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 | 34 | 2021 |
| DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning R Sass, E Bergman, A Biedenkapp, F Hutter, M Lindauer Workshop on Adaptive Experimental Design and Active Learning in the Real …, 2022 | 30 | 2022 |
| 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 | 25 | 2019 |
Frank HutterPrior Labs; ELLIS Institute Tübingen; University of FreiburgVerified email at cs.uni-freiburg.de
