André Biedenkapp

According to our database, André Biedenkapp authored at least 29 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalization.

Sai Prasanna, Karim Farid, Raghu Rajan, André Biedenkapp

CoRR, 2024

Hierarchical Transformers are Efficient Meta-Reinforcement Learners.

Gresa Shala, André Biedenkapp, Josif Grabocka

CoRR, 2024

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

Gray-Box Gaussian Processes for Automated Reinforcement Learning.

Gresa Shala, André Biedenkapp, Frank Hutter, Josif Grabocka

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Dynamic algorithm configuration by reinforcement learning.

André Biedenkapp

PhD thesis, 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

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

Automated Dynamic Algorithm Configuration.

Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter

J. Artif. Intell. Res., 2022

DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning.

René Sass, Eddie Bergman, André Biedenkapp, Frank Hutter, Marius Lindauer

CoRR, 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

Theory-inspired parameter control benchmarks for dynamic algorithm configuration.

André Biedenkapp, Nguyen Dang, Martin S. Krejca, Frank Hutter, Carola Doerr

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

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

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

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

In-Loop Meta-Learning with Gradient-Alignment Reward.

Samuel Müller, André Biedenkapp, Frank Hutter

CoRR, 2021

DACBench: A Benchmark Library for Dynamic Algorithm Configuration.

Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Self-Paced Context Evaluation for Contextual Reinforcement Learning.

Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer

Proceedings of the 38th International Conference on Machine Learning, 2021

TempoRL: Learning When to Act.

André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer

Proceedings of the 38th International Conference on Machine Learning, 2021

Sample-Efficient Automated Deep Reinforcement Learning.

Jörg K. H. Franke, Gregor Köhler, André Biedenkapp, Frank Hutter

Proceedings of the 9th International Conference on Learning Representations, 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

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Learning Heuristic Selection with Dynamic Algorithm Configuration.

David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer

Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling, 2021

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, Carola Doerr, Marius Lindauer, Frank Hutter

CoRR, 2020

Learning Step-Size Adaptation in CMA-ES.

Gresa Shala, André Biedenkapp, Noor H. Awad, Steven Adriaensen, Marius Lindauer, Frank Hutter

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework.

André Biedenkapp, H. Furkan Bozkurt, Theresa Eimer, Frank Hutter, Marius Lindauer

Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

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, Frank Hutter

CoRR, 2019

Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters.

Marius Lindauer, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Frank Hutter

CoRR, 2019

Towards White-box Benchmarks for Algorithm Control.

André Biedenkapp, H. Furkan Bozkurt, Frank Hutter, Marius Lindauer

CoRR, 2019

2018

CAVE: Configuration Assessment, Visualization and Evaluation.

Andre Biedenkapp, Joshua Marben, Marius Lindauer, Frank Hutter

Proceedings of the Learning and Intelligent Optimization - 12th International Conference, 2018

2017

Efficient Parameter Importance Analysis via Ablation with Surrogates.

Andre Biedenkapp, Marius Lindauer, Katharina Eggensperger, Frank Hutter, Chris Fawcett, Holger H. Hoos

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017