Zeige Ergebnisse 1 -
15
von 15
2024
Becktepe J, Dierkes J, Benjamins C, Mohan A, Salinas D, Rajan R et al. ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. in 17th European Workshop on Reinforcement Learning (EWRL 2024). 2024 Epub 2024.
Eimer T, Hutter F, Lindauer M, Biedenkapp A, Erfinder/-innen. Verfahren zum Trainieren eines Algorithmus des maschinellen Lernens durch ein bestärkendes Lernverfahren. DE102022210480A1. 2024 Apr 4.
Tornede A, Deng D, Eimer T, Giovanelli J, Mohan A, Ruhkopf T et al. AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. Transactions on Machine Learning Research. 2024 Feb 9. Epub 2024 Feb 9. doi: 10.48550/arXiv.2306.08107
2023
Benjamins C, Eimer T, Schubert FG, Mohan A, Döhler S, Biedenkapp A et al. Contextualize Me – The Case for Context in Reinforcement Learning. Transactions on Machine Learning Research. 2023 Jun 5;2023(6). Epub 2023 Jun 5. doi: 10.48550/arXiv.2202.04500
Benjamins C, Eimer T, Schubert FG, Mohan A, Döhler S, Biedenkapp A et al. Extended Abstract: Contextualize Me -- The Case for Context in Reinforcement Learning. in The 16th European Workshop on Reinforcement Learning (EWRL 2023). 2023 Epub 2023.
Eimer T, Lindauer M, Raileanu R. Extended Abstract: Hyperparameters in Reinforcement Learning and How To Tune Them. in The 16th European Workshop on Reinforcement Learning (EWRL 2023). 2023 Epub 2023.
Eimer T, Lindauer M, Raileanu R. Hyperparameters in Reinforcement Learning and How to Tune Them. in ICML'23: Proceedings of the 40th International Conference on Machine Learning. 2023. S. 9104–9149. 366 doi: 10.48550/arXiv.2306.01324, 10.5555/3618408.3618774
2022
Adriaensen S, Biedenkapp A, Shala G, Awad N, Eimer T, Lindauer M et al. Automated Dynamic Algorithm Configuration. Journal of Artificial Intelligence Research. 2022 Dez;75:1633-1699. doi: 10.48550/arXiv.2205.13881, 10.1613/jair.1.13922
Parker-Holder J, Rajan R, Song X, Biedenkapp A, Miao Y, Eimer T et al. Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. Journal of Artificial Intelligence Research. 2022 Jun 1;74(74):517-568. doi: 10.48550/arXiv.2201.03916, 10.1613/jair.1.13596
2021
Benjamins C, Eimer T, Schubert F, Biedenkapp A, Rosenhahn B, Hutter F et al. CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. in Workshop on Ecological Theory of Reinforcement Learning, NeurIPS 2021. 2021 Epub 2021 Okt 5.
Eimer T, Biedenkapp A, Reimer M, Adriaensen S, Hutter F, Lindauer MT. DACBench: A Benchmark Library for Dynamic Algorithm Configuration. in Zhou ZH, Hrsg., Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21). 2021. S. 1668-1674. (IJCAI International Joint Conference on Artificial Intelligence). doi: 10.24963/ijcai.2021/230
Eimer T, Benjamins C, Lindauer MT. Hyperparameters in Contextual RL are Highly Situational. in International Workshop on Ecological Theory of RL (at NeurIPS). 2021 doi: 10.48550/arXiv.2212.10876
Eimer T, Biedenkapp A, Hutter F, Lindauer M. Self-Paced Context Evaluation for Contextual Reinforcement Learning. in Proceedings of the international conference on machine learning (ICML). ML Research Press. 2021. (Proceedings of Machine Learning Research).
Schubert F, Eimer T, Rosenhahn B, Lindauer M. Automatic Risk Adaptation in Distributional Reinforcement Learning. 2021 Jun 11. Epub 2021 Jun 11.
2020
Biedenkapp A, Bozkurt HF, Eimer T, Hutter F, Lindauer MT. Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework. in De Giacomo G, Catala A, Dilkina B, Milano M, Barro S, Bugarin A, Lang J, Hrsg., ECAI 2020 - 24th European Conference on Artificial Intelligence. 2020. S. 427-434. (Frontiers in Artificial Intelligence and Applications). doi: 10.3233/FAIA200122
