Theresa Eimer
Adresse
Welfengarten 1
30167 Hannover
Gebäude
Raum
Theresa Eimer
Adresse
Welfengarten 1
30167 Hannover
Gebäude
Raum

I am interested in making efficient and robust learning agents at the intersection of AutoML and Reinforcement Learning. My research interests include: (Meta-)Reinforcement Learning, AutoRL and Dynamic Algorithm Configuration.

Research Interests

  • Generalization in Reinforcement Learning
  • Dynamic Algorithm Configuration
  • Automated Reinforcement Learning
  • Meta Reinforcement Learning
  • Societal Impact of Machine Learning
  • Work Experience

    2022 - 2023: Research Intern at Meta AI London with Roberta Raileanu

    2022: Chair for Diversity & Inclusion at the AutoML-Conf

    since 2020: Doctoral Researcher at the Leibniz University Hannover

  • Education

    since 2020: PhD Candidate at the Institute for AI, Leibniz University Hannover

    2016 - 2019: M.Sc. Computer Science at the Albert-Ludwigs University Freiburg
    Thesis: Improved Meta-Learning for Dynamic Algorithm Configuration
    Supervisor: Prof. Dr. Frank Hutter

    2013 - 2016: B.Sc. Computer Science at the University of Hamburg
    Thesis: On Thue Numbers
    Supervisor: Dr. Frank Heitmann

  • Office Hour

    If you'd like to talk to me about a project, a class you're taking or something else, feel free to book a 30 minute slot in my calendar.
    This office hour is open to anyone, whether you're taking a course with me or not.
    For longer meetings or different time slots, please contact me via e-mail.

Publications

Zeige Ergebnisse 1 - 14 von 14

2024


Eimer T, Hutter F, Lindauer M, Biedenkapp A, Erfinder/-innen. Verfahren zum Trainieren eines Algorithmus des maschinellen Lernens durch ein bestärkendes Lernverfahren. DE102022210480. 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). 2021 Epub 2021.
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