André Biedenkapp

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 37

Top Topics

Reinforcement Learning

Top Venues

J. Artif. Intell. Res.

Publications

Philipp Bordne, M. Asif Hasan, Eddie Bergman, Noor H. Awad, André Biedenkapp
CANDID DAC: Leveraging Coupled Action Dimensions with Importance Differences in DAC. CoRR (2024)
Gresa Shala, André Biedenkapp, Josif Grabocka
Hierarchical Transformers are Efficient Meta-Reinforcement Learners. CoRR (2024)
Sai Prasanna, Karim Farid, Raghu Rajan, André Biedenkapp
Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalization. CoRR (2024)
Tidiane Camaret Ndir, André Biedenkapp, Noor H. Awad
Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement Learning. CoRR (2024)
Raghu Rajan, Jessica Lizeth Borja Diaz, Suresh Guttikonda, Fabio Ferreira, André Biedenkapp, Jan Ole von Hartz, Frank Hutter
MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning. J. Artif. Intell. Res. 77 (2023)
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)
Gresa Shala, André Biedenkapp, Frank Hutter, Josif Grabocka
Gray-Box Gaussian Processes for Automated Reinforcement Learning. ICLR (2023)
Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter
Automated Dynamic Algorithm Configuration. CoRR (2022)
André Biedenkapp, Nguyen Dang, Martin S. Krejca, Frank Hutter, Carola Doerr
Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration. CoRR (2022)
Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. CoRR (2022)
Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter
Automated Dynamic Algorithm Configuration. J. Artif. Intell. Res. 75 (2022)
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 (2022)
René Sass, Eddie Bergman, André Biedenkapp, Frank Hutter, Marius Lindauer
DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. CoRR (2022)
André Biedenkapp, Nguyen Dang, Martin S. Krejca, Frank Hutter, Carola Doerr
Theory-inspired parameter control benchmarks for dynamic algorithm configuration. GECCO (2022)
Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. J. Artif. Intell. Res. 74 (2022)
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 (2022)
Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan O. Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning. CoRR (2021)
Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer
DACBench: A Benchmark Library for Dynamic Algorithm Configuration. IJCAI (2021)
André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer
TempoRL: Learning When to Act. ICML (2021)
André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer
TempoRL: Learning When to Act. CoRR (2021)
David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer
Learning Heuristic Selection with Dynamic Algorithm Configuration. ICAPS (2021)
Jörg K. H. Franke, Gregor Köhler, André Biedenkapp, Frank Hutter
Sample-Efficient Automated Deep Reinforcement Learning. ICLR (2021)
Samuel Müller, André Biedenkapp, Frank Hutter
In-Loop Meta-Learning with Gradient-Alignment Reward. CoRR (2021)
Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer
DACBench: A Benchmark Library for Dynamic Algorithm Configuration. CoRR (2021)
Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan O. Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning. AISTATS (2021)
Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. CoRR (2021)
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 (2021)
Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer
Self-Paced Context Evaluation for Contextual Reinforcement Learning. CoRR (2021)
Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer
Self-Paced Context Evaluation for Contextual Reinforcement Learning. ICML (2021)
David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer
Learning Heuristic Selection with Dynamic Algorithm Configuration. CoRR (2020)
André Biedenkapp, H. Furkan Bozkurt, Theresa Eimer, Frank Hutter, Marius Lindauer
Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework. ECAI (2020)
Noor H. Awad, Gresa Shala, Difan Deng, Neeratyoy Mallik, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Diederick Vermetten, Hao Wang, Carola Doerr, Marius Lindauer, Frank Hutter
Squirrel: A Switching Hyperparameter Optimizer. CoRR (2020)
Gresa Shala, André Biedenkapp, Noor H. Awad, Steven Adriaensen, Marius Lindauer, Frank Hutter
Learning Step-Size Adaptation in CMA-ES. PPSN (1) (2020)
Jörg K. H. Franke, Gregor Köhler, André Biedenkapp, Frank Hutter
Sample-Efficient Automated Deep Reinforcement Learning. CoRR (2020)
Marius Lindauer, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Frank Hutter
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters. CoRR (2019)
Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Joshua Marben, Philipp Müller, Frank Hutter
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters. CoRR (2019)
André Biedenkapp, H. Furkan Bozkurt, Frank Hutter, Marius Lindauer
Towards White-box Benchmarks for Algorithm Control. CoRR (2019)