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ORCIDCarolin Benjamins
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2020 – today
- 2024
[c4]Carolin Benjamins
, Gjorgjina Cenikj, Ana Nikolikj, Aditya Mohan, Tome Eftimov, Marius Lindauer:
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. GECCO Companion 2024: 563-566- [i11]Carolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj, Aditya Mohan, Tome Eftimov, Marius Lindauer:
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. CoRR abs/2407.13513 (2024) - [i10]Jannis Becktepe, Julian Dierkes, Carolin Benjamins, Aditya Mohan, David Salinas, Raghu Rajan, Frank Hutter, Holger H. Hoos, Marius Lindauer, Theresa Eimer:
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. CoRR abs/2409.18827 (2024) - 2023
- [j3]Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, Sebastian Döhler, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer:
Contextualize Me - The Case for Context in Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023) - [j2]Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer:
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c3]Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer:
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. AutoML 2023: 6/1-50 - [c2]Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer:
AutoRL Hyperparameter Landscapes. AutoML 2023: 13/1-27 - [c1]Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer:
Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. GECCO Companion 2023: 483-486 - [i9]Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer:
AutoRL Hyperparameter Landscapes. CoRR abs/2304.02396 (2023) - [i8]Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer:
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. CoRR abs/2306.04262 (2023) - 2022
- [j1]Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter:
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. J. Mach. Learn. Res. 23: 54:1-54:9 (2022) - [i7]Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer:
Contextualize Me - The Case for Context in Reinforcement Learning. CoRR abs/2202.04500 (2022) - [i6]Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer:
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. CoRR abs/2205.11357 (2022) - [i5]Carolin Benjamins, Elena Raponi, Anja Jankovic, Koen van der Blom, Maria Laura Santoni, Marius Lindauer, Carola Doerr:
PI is back! Switching Acquisition Functions in Bayesian Optimization. CoRR abs/2211.01455 (2022) - [i4]Carolin Benjamins, Anja Jankovic, Elena Raponi, Koen van der Blom, Marius Lindauer, Carola Doerr:
Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. CoRR abs/2211.09678 (2022) - [i3]Theresa Eimer, Carolin Benjamins, Marius Lindauer:
Hyperparameters in Contextual RL are Highly Situational. CoRR abs/2212.10876 (2022) - 2021
- [i2]Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, René Sass, Frank Hutter:
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. CoRR abs/2109.09831 (2021) - [i1]Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer:
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. CoRR abs/2110.02102 (2021)
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