PhD Student

André Biedenkapp

Postal address

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

Fax

+49 761 203-74217

Office

Building 74, Room 00-012
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About

Since October 2017 I am a PhD student at the Machine Learning Group under the supervision of Frank Hutter and Marius Lindauer. Before that I completed my master and bachelor degrees in computer science at the University of Freiburg.

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

  • Leading:
    • DAC: Dynamic algorithm configuration via reinforcement learning and accompanying white-box benchmarks.
    • PyImp: A python package to determine parameter importance based on configuration data.
    • CAVE: CAVE aims to help algorithm and configurator developers to better understand their experimental setup in an automated fashion. An example of a CAVE result can be found here. The video presentation for the corresponding CAVE paper as shown at LION'18 can be viewed here.
  • Involved:
    • 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: 4 (Holger H. Hoos -> Therese C. Biedl -> Jeffrey O. Shallit -> Paul Erdös)
My 3 word address: paid.laptops.brothers

Publications

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.

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

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

In: arXiv:2109.09831, 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.

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.

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.

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 Journal Article

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.

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

MDP Playground: Controlling Dimensions of Hardness in Reinforcement Learning Inproceedings

In: arXiv:1909.07750, 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

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

Towards White-box Benchmarks for Algorithm Control Inproceedings

In: IJCAI 2019 DSO Workshop, 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.

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.