| 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 | 168 | 2022 |
| Hyperparameters in Reinforcement Learning and How To Tune Them T Eimer, M Lindauer, R Raileanu International Conference on Machine Learning (ICML) 2023, 2023 | 126 | 2023 |
| 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 | 109 | 2020 |
| Contextualize Me--The Case for Context in Reinforcement Learning C Benjamins, T Eimer, F Schubert, A Mohan, A Biedenkapp, ... Transactions on Machine Learning Research, 2023 | 71 | 2023 |
| AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks A Tornede, D Deng, T Eimer, J Giovanelli, A Mohan, T Ruhkopf, S Segel, ... Transactions on Machine Learning Research (TMLR), 2024 | 70 | 2024 |
| 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 |
| Self-Paced Context Evaluation for Contextual Reinforcement Learning T Eimer, A Biedenkapp, F Hutter, M Lindauer International Conference on Machine Learning (ICML 2021), 2021 | 47 | 2021 |
| DACBench: A Benchmark Library for Dynamic Algorithm Configuration T Eimer, A Biedenkapp, M Reimer, S Adriaensen, F Hutter, M Lindauer 30th International Joint Conferences on Artificial Intelligence (IJCAI 2021), 2021 | 39 | 2021 |
| Carl: A Benchmark for Contextual and Adaptive Reinforcement Learning C Benjamins, T Eimer, F Schubert, A Biedenkapp, B Rosenhahn, F Hutter, ... EcoRL@NeurIPS 2021, 2021 | 34 | 2021 |
| Automatic Risk Adaptation in Distributional Reinforcement Learning F Schubert, T Eimer, B Rosenhahn, M Lindauer RL4RealLife Workshop ICML 2021, 2021 | 10 | 2021 |
| Hyperparameters in Contextual RL are Highly Situational T Eimer, C Benjamins, M Lindauer EcoRL Workshop @ NeurIPS'21, 2021 | 9* | 2021 |
| ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning J Becktepe, J Dierkes, C Benjamins, A Mohan, D Salinas, R Rajan, ... European Workshop on Reinforcement Learning (EWRL), 2024 | 3* | 2024 |
| Growing with Experience: Growing Neural Networks in Deep Reinforcement Learning L Fehring, M Lindauer, T Eimer Multi-disciplinary Conference on Reinforcement Learning and Decision Making …, 2025 | 1 | 2025 |
| Revisiting Learning Rate Control M Henheik, T Eimer, M Lindauer AutoML Methods Track, 2025 | 1 | 2025 |
| Best Practices For Empirical Meta-Algorithmic Research Guidelines from the COSEAL Research Network T Eimer, L Schäpermeier, A Biedenkapp, A Tornede, L Kotthoff, P Leyman, ... arXiv preprint arXiv:2512.16491, 2025 | | 2025 |
| Task Scheduling & Forgetting in Multi-Task Reinforcement Learning M Speckmann, T Eimer Multi-disciplinary Conference on Reinforcement Learning and Decision Making …, 2025 | | 2025 |
| Performance Prediction In Reinforcement Learning: The Bad And The Ugly J Dierkes, T Eimer, M Lindauer, H Hoos European Workshop on Reinforcement Learning (EWRL), 2025 | | 2025 |
| Mighty: A Comprehensive Tool for studying Generalization, Meta-RL and AutoRL A Mohan, T Eimer, C Benjamins, M Lindauer, A Biedenkapp European Workshop on Reinforcement Learning (EWRL), 2025 | | 2025 |
| Reinforcing Automated Machine Learning - Bridging AutoML and Reinforcement Learning T Eimer Hannover: Institutionelles Repositorium der Leibniz Universität Hannover, 2024 | | 2024 |
| Towards Self-Paced Context Evaluation for Contextual Reinforcement Learning T Eimer, A Biedenkapp, F Hutter, M Lindauer BIG@ICML 2020, 2020 | | 2020 |
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence LUH|AI, L3S ResearchVerified email at ai.uni-hannover.de
