Theresa Eimer-Rüegg

Phone+49 511 762 17321
Emailt.eimerai.uni-hannoverde

11 Conference contribution
4 Article
2 Conference abstract
2 Preprint
More

Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL
Che, M., Tseng, T.-Y., Eimer-Rüegg, T., Lindauer, M. & von Rohr, A., 13 May 2026, (Accepted/In press).
Research output: Contribution to conference › Paper › Research › peer review
Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network
Eimer, T., Schäpermeier, L., Biedenkapp, A., Tornede, A., Kotthoff, L., Leyman, P., Feurer, M., Eggensperger, K., Maile, K., Tornede, T., Kozak, A., Xue, K., Wever, M. D., Baratchi, M., Pulatov, D., Trautmann, H., Kashgarani, H. & Lindauer, M., 2025, (E-pub ahead of print).
Research output: Working paper/Preprint › Preprint

Open Access
Growing with Experience: Growing Neural Networks in Deep Reinforcement Learning
Fehring, L., Eimer, T. & Lindauer, M., 15 Feb 2025, (Accepted/In press) 2025 Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2025).
Research output: Chapter in book/report/conference proceeding › Conference abstract › Research › peer review
Mighty: A Comprehensive Tool for studying Generalization, Meta-RL and AutoRL
Mohan, A., Eimer, T., Benjamins, C., Lindauer, M. & Biedenkapp, A., Sept 2025, (E-pub ahead of print) 18th European Workshop on Reinforcement Learning (EWRL).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

Open Access
Performance Prediction In Reinforcement Learning: The Bad And The Ugly
Dierkes, J., Eimer, T., Lindauer, M. & Hoos, H., Sept 2025, (E-pub ahead of print) 18th European Workshop on Reinforcement Learning (EWRL).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

Open Access

Algorithm Control: Efficient Learning to Control Algorithm Parameters
Lindauer, M. T. (Principal Investigator), Eimer-Rüegg, T. (Project staff), Benjamins, C. (Project staff) & Mohan, A. (Project staff)
1 Oct 2020 → 14 Sept 2024
Project: Research

Theresa Eimer-Rüegg

Research output

Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL

Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network

Growing with Experience: Growing Neural Networks in Deep Reinforcement Learning

Mighty: A Comprehensive Tool for studying Generalization, Meta-RL and AutoRL

Performance Prediction In Reinforcement Learning: The Bad And The Ugly

Projects

Algorithm Control: Efficient Learning to Control Algorithm Parameters