Efficient Parameter Importance Analysis via Ablation with Surrogates. - Andre Biedenkapp; Marius Lindauer; Katharina Eggensperger; Frank Hutter; Chris Fawcett; Holger H. Hoos (AAAI, 2017)

CAVE: Configuration Assessment, Visualization and Evaluation. - Andre Biedenkapp; Joshua Marben; Marius Lindauer; Frank Hutter (LION, 2018)

Towards White-box Benchmarks for Algorithm Control. - André Biedenkapp; H. Furkan Bozkurt; Frank Hutter; Marius Lindauer (CoRR, 2019)

Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters. - Marius Lindauer; Matthias Feurer; Katharina Eggensperger; André Biedenkapp; Frank Hutter (CoRR, 2019)

BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters. - Marius Lindauer; Katharina Eggensperger; Matthias Feurer; André Biedenkapp; Joshua Marben; Philipp Müller 008; Frank Hutter (CoRR, 2019)

Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework. - André Biedenkapp; H. Furkan Bozkurt; Theresa Eimer; Frank Hutter; Marius Lindauer (ECAI, 2020)

Learning Step-Size Adaptation in CMA-ES. - Gresa Shala; André Biedenkapp; Noor H. Awad; Steven Adriaensen; Marius Lindauer; Frank Hutter (PPSN (1), 2020)

Squirrel: A Switching Hyperparameter Optimizer. - Noor H. Awad; Gresa Shala; Difan Deng; Neeratyoy Mallik; Matthias Feurer; Katharina Eggensperger; André Biedenkapp; Diederick Vermetten; Hao Wang 025; Carola Doerr; Marius Lindauer; Frank Hutter (CoRR, 2020)

Learning Heuristic Selection with Dynamic Algorithm Configuration. - David Speck 001; André Biedenkapp; Frank Hutter; Robert Mattmüller; Marius Lindauer (ICAPS, 2021)

On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning. - Baohe Zhang; Raghu Rajan; Luis Pineda; Nathan O. Lambert; André Biedenkapp; Kurtland Chua; Frank Hutter; Roberto Calandra (AISTATS, 2021)

Sample-Efficient Automated Deep Reinforcement Learning. - Jörg K. H. Franke; Gregor Köhler; André Biedenkapp; Frank Hutter (ICLR, 2021)

TempoRL: Learning When to Act. - André Biedenkapp; Raghu Rajan; Frank Hutter; Marius Lindauer (ICML, 2021)

Self-Paced Context Evaluation for Contextual Reinforcement Learning. - Theresa Eimer; André Biedenkapp; Frank Hutter; Marius Lindauer (ICML, 2021)

DACBench: A Benchmark Library for Dynamic Algorithm Configuration. - Theresa Eimer; André Biedenkapp; Maximilian Reimer; Steven Adriaensen; Frank Hutter; Marius Lindauer (IJCAI, 2021)

In-Loop Meta-Learning with Gradient-Alignment Reward. - Samuel Müller 005; André Biedenkapp; Frank Hutter (CoRR, 2021)

Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization. - Julia Guerrero-Viu; Sven Hauns; Sergio Izquierdo; Guilherme Miotto; Simon Schrodi; Andre Biedenkapp; Thomas Elsken; Difan Deng; Marius Lindauer; Frank Hutter (CoRR, 2021)

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)

Theory-inspired parameter control benchmarks for dynamic algorithm configuration. - André Biedenkapp; Nguyen Dang 001; Martin S. Krejca; Frank Hutter; Carola Doerr (GECCO, 2022)

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)

DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. - René Sass; Eddie Bergman; André Biedenkapp; Frank Hutter; Marius Lindauer (CoRR, 2022)

Automated Dynamic Algorithm Configuration. - Steven Adriaensen; André Biedenkapp; Gresa Shala; Noor H. Awad; Theresa Eimer; Marius Lindauer; Frank Hutter (J. Artif. Intell. Res., 2022)

Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. - 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 (J. Artif. Intell. Res., 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)

Dynamic algorithm configuration by reinforcement learning. - André Biedenkapp (2022)

Gray-Box Gaussian Processes for Automated Reinforcement Learning. - Gresa Shala; André Biedenkapp; Frank Hutter; Josif Grabocka (ICLR, 2023)

MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning. - Raghu Rajan; Jessica Lizeth Borja Diaz; Suresh Guttikonda; Fabio Ferreira; André Biedenkapp; Jan Ole von Hartz; Frank Hutter (J. Artif. Intell. Res., 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)

HPO-RL-Bench: A Zero-Cost Benchmark for HPO in Reinforcement Learning. - Gresa Shala; Sebastian Pineda-Arango; André Biedenkapp; Frank Hutter; Josif Grabocka (AutoML, 2024)

Dreaming of Many Worlds: Learning Contextual World Models aids Zero-Shot Generalization. - Sai Prasanna; Karim Farid; Raghu Rajan; André Biedenkapp (RLJ, 2024)

Hierarchical Transformers are Efficient Meta-Reinforcement Learners. - Gresa Shala; André Biedenkapp; Josif Grabocka (CoRR, 2024)

Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement Learning. - Tidiane Camaret Ndir; André Biedenkapp; Noor H. Awad (CoRR, 2024)

CANDID DAC: Leveraging Coupled Action Dimensions with Importance Differences in DAC. - Philipp Bordne; M. Asif Hasan; Eddie Bergman; Noor H. Awad; André Biedenkapp (CoRR, 2024)

One-shot World Models Using a Transformer Trained on a Synthetic Prior. - Fabio Ferreira; Moreno Schlageter; Raghu Rajan; Andre Biedenkapp; Frank Hutter (CoRR, 2024)

On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+(λ, λ))-GA. - Tai Nguyen 008; Phong Le; André Biedenkapp; Carola Doerr; Nguyen Dang 001 (GECCO, 2025)

Efficient Cross-Episode Meta-RL. - Gresa Shala; André Biedenkapp; Pierre Krack; Florian Walter; Josif Grabocka (ICLR, 2025)

Meta-learning Population-based Methods for Reinforcement Learning. - Johannes Hog; Raghu Rajan; André Biedenkapp; Noor H. Awad; Frank Hutter; Vu Nguyen (Trans. Mach. Learn. Res., 2025)

A Llama walks into the 'Bar': Efficient Supervised Fine-Tuning for Legal Reasoning in the Multi-state Bar Exam. - Rean Fernandes; André Biedenkapp; Frank Hutter; Noor H. Awad (CoRR, 2025)