SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. - Marius Lindauer; Katharina Eggensperger; Matthias Feurer; André Biedenkapp; Difan Deng; Carolin Benjamins; René Sass; Frank Hutter (CoRR, 2021)

CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. - Carolin Benjamins; Theresa Eimer; Frederik Schubert; André Biedenkapp; Bodo Rosenhahn; Frank Hutter; Marius Lindauer (CoRR, 2021)

Contextualize Me - The Case for Context in Reinforcement Learning. - Carolin Benjamins; Theresa Eimer; Frederik Schubert; Aditya Mohan; André Biedenkapp; Bodo Rosenhahn; Frank Hutter; Marius Lindauer (CoRR, 2022)

PI is back! Switching Acquisition Functions in Bayesian Optimization. - Carolin Benjamins; Elena Raponi; Anja Jankovic 001; Koen van der Blom; Maria Laura Santoni; Marius Lindauer; Carola Doerr (CoRR, 2022)

Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. - Carolin Benjamins; Anja Jankovic 001; Elena Raponi; Koen van der Blom; Marius Lindauer; Carola Doerr (CoRR, 2022)

Hyperparameters in Contextual RL are Highly Situational. - Theresa Eimer; Carolin Benjamins; Marius Lindauer (CoRR, 2022)

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. - Marius Lindauer; Katharina Eggensperger; Matthias Feurer; André Biedenkapp; Difan Deng; Carolin Benjamins; Tim Ruhkopf; René Sass; Frank Hutter (J. Mach. Learn. Res., 2022)

Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. - Carolin Benjamins; Elena Raponi; Anja Jankovic 001; Carola Doerr; Marius Lindauer (AutoML, 2023)

AutoRL Hyperparameter Landscapes. - Aditya Mohan; Carolin Benjamins; Konrad Wienecke; Alexander Dockhorn; Marius Lindauer (AutoML, 2023)

Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. - Carolin Benjamins; Elena Raponi; Anja Jankovic 001; Carola Doerr; Marius Lindauer (GECCO Companion, 2023)

Contextualize Me - The Case for Context in Reinforcement Learning. - Carolin Benjamins; Theresa Eimer; Frederik Schubert; Aditya Mohan; Sebastian Döhler; André Biedenkapp; Bodo Rosenhahn; Frank Hutter; Marius Lindauer (Trans. Mach. Learn. Res., 2023)

POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. - Frederik Schubert; Carolin Benjamins; Sebastian Döhler; Bodo Rosenhahn; Marius Lindauer (Trans. Mach. Learn. Res., 2023)

Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. - Carolin Benjamins; Gjorgjina Cenikj; Ana Nikolikj; Aditya Mohan; Tome Eftimov; Marius Lindauer (GECCO Companion, 2024)

ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. - Jannis Becktepe; Julian Dierkes; Carolin Benjamins; Aditya Mohan; David Salinas; Raghu Rajan; Frank Hutter; Holger H. Hoos; Marius Lindauer; Theresa Eimer (CoRR, 2024)

SynthACticBench: A Capability-Based Synthetic Benchmark for Algorithm Configuration. - Valentin Margraf; Anna Lappe; Marcel Wever; Carolin Benjamins; Eyke Hüllermeier; Marius Lindauer (GECCO, 2025)

carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks. - Carolin Benjamins; Helena Graf; Sarah Segel; Difan Deng; Tim Ruhkopf; Leona Hennig; Soham Basu; Neeratyoy Mallik; Edward Bergman; Deyao Chen; François Clément; Matthias Feurer; et al. (CoRR, 2025)