11 - 16 out of 16Page size: 10
Publications
2023
- E-pub ahead of print
Symbolic Explanations for Hyperparameter Optimization
Segel, S., Graf, H., Tornede, A., Bischl, B. & Lindauer, M., 16 May 2023, (E-pub ahead of print) AutoML Conference 2023.Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- E-pub ahead of print
Extended Abstract: Hyperparameters in Reinforcement Learning and How To Tune Them
Eimer, T., Lindauer, M. & Raileanu, R., 2023, (E-pub ahead of print) The 16th European Workshop on Reinforcement Learning (EWRL 2023).Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
2022
- E-pub ahead of print
Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution
Moosbauer, J., Casalicchio, G., Lindauer, M. & Bischl, B., 11 Jun 2022, (E-pub ahead of print).Research output: Working paper/Preprint › Preprint
- E-pub ahead of print
Practitioner Motives to Use Different Hyperparameter Optimization Methods
Hasebrook, N., Morsbach, F., Kannengießer, N., Zöller, M., Franke, J., Lindauer, M., Hutter, F. & Sunyaev, A., 3 Mar 2022, (E-pub ahead of print) In: ACM Transactions on Computer-Human Interaction.Research output: Contribution to journal › Article › Research › peer review
- Published
PriorBand: HyperBand + Human Expert Knowledge
Mallik, N., Hvarfner, C., Stoll, D., Janowski, M., Bergman, E., Lindauer, M. T., Nardi, L. & Hutter, F., 2022, 2022 NeurIPS Workshop on Meta Learning (MetaLearn).Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- Published
π BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization.
Hvarfner, C., Stoll, D., Souza, A. L. F., Lindauer, M., Hutter, F. & Nardi, L., 2022, Proceedings of the International conference on Learning Representation (ICLR).Research output: Chapter in book/report/conference proceeding › Conference contribution › Research