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Postdoctoral Research Fellow

André Biedenkapp

Postal address

Institut für Informatik
Albert-Ludwigs-Universität Freiburg
Sekretariat Hutter/Maschinelles Lernen
Georges-Köhler-Allee 074
79110 Freiburg, Germany

Office

Building 074, Room 00-018
GoogleScholarORCIDGitHubMarker

About

I am a researcher at the University of Freiburg, Germany. My primary research interest is in the field of artificial intelligence, with a focus on automated machine learning and algorithm configuration, i.e., the problem of automatically tuning (machine learning) algorithms to maximize their performance. In particular, I focus on using reinforcement learning to tackle the problem of dynamically configuring algorithms.

I completed my bachelor’s degree in 2015 and my master’s degree in 2017 in computer science at the University of Freiburg. From February 2018 to October 2022, I did my Ph.D. at the University of Freiburg, at the Machine Learning Chair under the supervision of Prof. Dr. Frank Hutter and Prof. Dr. Marius Lindauer (Leibniz University Hannover). In October 2022 I successfully defended my PhD (Dr. rer. nat.) with the topic ‘Dynamic Algorithm Configuration by Reinforcement Learning’.

Research Interests

I am interested in all facets of artificial intelligence. My research focuses on new ways to control the behavior of algorithms online. More precisely my research areas include:

Code

  • DAC: Dynamic algorithm configuration via reinforcement learning and accompanying white-box benchmarks.
  • PyImp: A python package to determine parameter importance based on configuration data.
  • DACBench: benchmark library for Dynamic Algorithm Configuration with focus on reproducibility and comparability of DAC approaches.
  • SMAC v3: Python implementation of SMAC (Algorithm Configurator) for automatic tuning of parameter configurations on any kind of algorithms.

Teaching

Misc

Erdös number: 3 (Carola Doerr -> Shlomo Moran -> Paul Erdös)
My 3 word address: ///forecast.gamer.showcase

Publications

2022

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

Automated Dynamic Algorithm Configuration Journal Article

In: Journal of Artificial Intelligence Research (JAIR), vol. 75, pp. 1633-1699, 2022.

Shala, Gresa; Arango, Sebastian Pineda; Biedenkapp, André; Hutter, Frank; Grabocka, Josif

AutoRL-Bench 1.0 Inproceedings

In: Workshop on Meta-Learning (MetaLearn@NeurIPS'22), 2022.

Shala, Gresa; Biedenkapp, André; Hutter, Frank; Grabocka, Josif

Gray-Box Gaussian Processes for Automated Reinforcement Learning Inproceedings

In: Workshop on Meta-Learning (MetaLearn@NeurIPS'22), 2022.

Biedenkapp, André

Dynamic Algorithm Configuration by Reinforcement Learning PhD Thesis

University of Freiburg, Department of Computer Science, 2022.

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

DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning Inproceedings

In: Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML@ICML'22), 2022.

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

Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration Inproceedings

In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'22), 2022, (Won the best paper award in the GECH track).

Biedenkapp, André; Speck, David; Sievers, Silvan; Hutter, Frank; Lindauer, Marius; Seipp, Jendrik

Learning Domain-Independent Policies for Open List Selection Inproceedings

In: Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL @ ICAPS'22), 2022.

Parker-Holder, Jack; Rajan, Raghu; Song, Xingyou; Biedenkapp, André; Miao, Yingjie; Eimer, Theresa; Zhang, Baohe; Nguyen, Vu; Calandra, Roberto; Faust, Aleksandra; Hutter, Frank; Lindauer, Marius

Automated Reinforcement Learning (AutoRL): A Survey and Open Problems Journal Article

In: Journal of Artificial Intelligence Research (JAIR), vol. 74, pp. 517-568, 2022.

Benjamins, Carolin; Eimer, Theresa; Schubert, Frederik; Mohan, Aditya; Biedenkapp, André; Rosenhan, Bodo; Hutter, Frank; Lindauer, Marius

Contextualize Me – The Case for Context in Reinforcement Learning Journal Article

In: arXiv:2202.04500, 2022.

Lindauer, Marius; Eggensperger, Katharina; Feurer, Matthias; Biedenkapp, André; Deng, Difan; Benjamins, Carolin; Ruhkopf, Tim; Sass, René; Hutter, Frank

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization Journal Article

In: Journal of Machine Learning Research (JMLR) -- MLOSS, vol. 23, no. 54, pp. 1-9, 2022.

2021

Benjamins, Carolin; Eimer, Theresa; Schubert, Frederik; Biedenkapp, André; Rosenhan, Bodo; Hutter, Frank; Lindauer, Marius

CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning Inproceedings

In: Workshop on Ecological Theory of Reinforcement Learning (EcoRL@NeurIPS'21), 2021.

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

DACBench: A Benchmark Library for Dynamic Algorithm Configuration Inproceedings

In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI'21), ijcai.org, 2021.

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

Learning Heuristic Selection with Dynamic Algorithm Configuration Inproceedings

In: Proceedings of the 31st International Conference on Automated Planning and Scheduling (ICAPS'21), 2021.

Eimer, Theresa; Biedenkapp, André; Hutter, Frank; Lindauer, Marius

Self-Paced Context Evaluations for Contextual Reinforcement Learning Inproceedings

In: Proceedings of the 38th International Conference on Machine Learning (ICML 2021), 2021.

Izquierdo, Sergio; Guerrero-Viu, Julia; Hauns, Sven; Miotto, Guilherme; Schrodi, Simon; Biedenkapp, André; Elsken, Thomas; Deng, Difan; Lindauer, Marius; Hutter, Frank

Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization Inproceedings

In: Workshop on Automated Machine Learning (AutoML@ICML'21), 2021.

Biedenkapp, André; Rajan, Raghu; Hutter, Frank; Lindauer, Marius

TempoRL: Learning When to Act Inproceedings

In: Proceedings of the 38th International Conference on Machine Learning (ICML 2021), 2021.

Rajan, Raghu; Diaz, Jessica Lizeth Borja; Guttikonda, Suresh; Ferreira, Fabio; Biedenkapp, André; von Hartz, Jan Ole; Hutter, Frank

MDP Playground: A Design and Debug Testbed for Reinforcement Learning Inproceedings

In: arXiv:1909.07750, 2021.

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

Sample-Efficient Automated Deep Reinforcement Learning Journal Article

In: International Conference on Learning Representations (ICLR) 2021, 2021.

Zhang, Baohe; Rajan, Raghu; Pineda, Luis; Lambert, Nathan; Biedenkapp, André; Chua, Kurtland; Hutter, Frank; Calandra, Roberto

On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning Inproceedings

In: Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS)'21, 2021.

Müller, Samuel; Biedenkapp, André; Hutter, Frank

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

In: AAAI workshop on Meta-Learning Challenges, 2021.

2020

Awad, Noor; Shala, Gresa; Deng, Difan; Mallik, Neeratyoy; Feurer, Matthias; Eggensperger, Katharina; Biedenkapp, André; Vermetten, Diederick; Wang, Hao; Doerr, Carola; Lindauer, Marius; Hutter, Frank

Squirrel: A Switching Hyperparameter Optimizer Description of the entry by AutoML.org & IOHprofiler to the NeurIPS 2020 BBO challenge Journal Article

In: arXiv:2012.08180 [cs.LG], 2020, (Optimizer description for the NeurIPS 2020 BBO competition. Squirrel won the competition´s warm-starting friendly leaderboard.).

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

Learning Heuristic Selection with Dynamic Algorithm Configuration Inproceedings

In: Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL@ICAPS'20), 2020.

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

Learning Step-Size Adaptation in CMA-ES Inproceedings

In: Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN'20), 2020.

Biedenkapp, André; Rajan, Raghu; Hutter, Frank; Lindauer, Marius

Towards TempoRL: Learning When to Act Inproceedings

In: Workshop on Inductive Biases, Invariances and Generalization in RL (BIG@ICML'20), 2020.

Eimer, Theresa; Biedenkapp, André; Hutter, Frank; Lindauer, Marius

Towards Self-Paced Context Evaluations for Contextual Reinforcement Learning Inproceedings

In: Workshop on Inductive Biases, Invariances and Generalization in RL (BIG@ICML'20), 2020.

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

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

In: Proceedings of the Twenty-fourth European Conference on Artificial Intelligence (ECAI'20), 2020.

2019

Lindauer, Marius; Eggensperger, Katharina; Feurer, Matthias; Biedenkapp, André; Marben, Joshua; Müller, Philipp; Hutter, Frank

BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters Journal Article

In: arXiv:1908.06756 [cs.LG], 2019.

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

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

In: IJCAI 2019 DSO Workshop, 2019.

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

Towards White-box Benchmarks for Algorithm Control Inproceedings

In: IJCAI 2019 DSO Workshop, 2019.

2018

Biedenkapp, André; Marben, Joshua; Lindauer, Marius; Hutter, Frank

CAVE: Configuration Assessment, Visualization and Evaluation Inproceedings

In: Proceedings of the International Conference on Learning and Intelligent Optimization (LION'18), 2018.

2017

Biedenkapp, André; Lindauer, Marius; Eggensperger, Katharina; Fawcett, Chris; Hoos, Holger H; Hutter, Frank

Efficient Parameter Importance Analysis via Ablation with Surrogates Inproceedings

In: Proceedings of the Thirty-First Conference on Artificial Intelligence (AAAI'17), pp. 773–779, 2017.