Publications
- 2024
- E-pub ahead of print
Bayesian Optimisation for Protein Sequence Design: Gaussian Processes with Zero-Shot Protein Language Model Prior Mean
Benjamins, C., Surana, S., Bent, O., Lindauer, M. & Duckworth, P., Dec 2024, (E-pub ahead of print) NeurIPS Workshop on Time Series in the Age of Large Models.Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- E-pub ahead of print
Bayesian Optimization for Protein Sequence Design: Back to Simplicity with Gaussian Processes
Benjamins, C., Surana, S., Bent, O., Lindauer, M. & Duckworth, P., Dec 2024, (E-pub ahead of print) AI for Accelerated Materials Design - NeurIPS Workshop 2024.Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- E-pub ahead of print
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
Becktepe, J., Dierkes, J., Benjamins, C., Mohan, A., Salinas, D., Rajan, R., Hutter, F., Hoos, H., Lindauer, M. & Eimer, T., 2024, (E-pub ahead of print) 17th European Workshop on Reinforcement Learning (EWRL 2024).Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- E-pub ahead of print
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization
Benjamins, C., Cenikj, G., Nikolikj, A., Mohan, A., Eftimov, T. & Lindauer, M., 2024, (E-pub ahead of print) Genetic and Evolutionary Computation Conference (GECCO).Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- 2023
- Published
AutoRL Hyperparameter Landscapes
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A. & Lindauer, M., 12 Nov 2023, Conference Proceedings - Second International Conference on Automated Machine Learning. 27 p. (Proceedings of Machine Learning Research; vol. 228).Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- Accepted/In press
Extended Abstract: AutoRL Hyperparameter Landscapes
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A. & Lindauer, M., 15 Sept 2023, (Accepted/In press) The 16th European Workshop on Reinforcement Learning (EWRL 2023).Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- Published
Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools
Denkena, B., Dittrich, M., Noske, H., Lange, D., Benjamins, C. & Lindauer, M., Jul 2023, In: The international journal of advanced manufacturing technology. 127, 3-4, p. 1143-1164 22 p.Research output: Contribution to journal › Article › Research › peer review
- E-pub ahead of print
Contextualize Me – The Case for Context in Reinforcement Learning
Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F. & Lindauer, M., 5 Jun 2023, (E-pub ahead of print) In: Transactions on Machine Learning Research. 2023, 6Research output: Contribution to journal › Article › Research › peer review
- Published
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning
Schubert, F., Benjamins, C., Döhler, S., Rosenhahn, B. & Lindauer, M., Apr 2023, In: Transactions on Machine Learning Research. 2023, 4Research output: Contribution to journal › Article › Research › peer review
- E-pub ahead of print
Extended Abstract: Contextualize Me -- The Case for Context in Reinforcement Learning
Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F. & Lindauer, M., 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
- Accepted/In press
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C. & Lindauer, M., 2023, (Accepted/In press) AutoML Conference 2023.Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- Accepted/In press
Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C. & Lindauer, M., 2023, (Accepted/In press) GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference Companion.Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- 2022
- Published
Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis
Benjamins, C., Jankovic, A., Raponi, E., Blom, K. V. D., Lindauer, M. & Doerr, C., 17 Nov 2022.Research output: Contribution to conference › Paper › Research › peer review
- E-pub ahead of print
PI is back! Switching Acquisition Functions in Bayesian Optimization
Benjamins, C., Raponi, E., Jankovic, A., Blom, K. V. D., Santoni, M. L., Lindauer, M. & Doerr, C., 2 Nov 2022, (E-pub ahead of print).Research output: Working paper/Preprint › Preprint
- Published
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Sass, R. & Hutter, F., Feb 2022, In: Journal of Machine Learning Research. 2022, 23, 8 p.Research output: Contribution to journal › Article › Research › peer review
- 2021
- E-pub ahead of print
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
Benjamins, C., Eimer, T., Schubert, F., Biedenkapp, A., Rosenhahn, B., Hutter, F. & Lindauer, M., 5 Oct 2021, (E-pub ahead of print) Workshop on Ecological Theory of Reinforcement Learning, NeurIPS 2021. 20 p.Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- Published
Hyperparameters in Contextual RL are Highly Situational
Eimer, T., Benjamins, C. & Lindauer, M. T., 2021, International Workshop on Ecological Theory of RL (at NeurIPS).Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review