André Biedenkapp

PhD student at the Machine Learning Lab in Freiburg.


Room 00-012

Georges-Köhler-Allee 74

79110 Freiburg, Germany

Since October 2017 I am a PhD student at the Machine Learning Group in Freiburg 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.

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:

  • Dynamic Algorithm Configuration
  • Learning to Learn
  • (Deep) Reinforcement Learning
  • Automated Machine Learning and Reinforcement Learning


Jun 24, 2022 We just uploaded the talk for ou GECCO’22 paper Theory-Inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration which is nominated for the best paper award.
Jun 19, 2022 Checkout our new paper on DeepCAVE, a tool to analyze and explain AutoML meta-data, which was just accepted at the ReALML@ICML workshop.
Jun 3, 2022 I am co-organizing the 2nd AutoML Fall School. The fall school will be held from 10th - 13th of October. Checkout the exciting list of invited speakers and hands-on sessions.
Jun 3, 2022 We just released a new paper on DAC for AI Planning. I’ll present the paper on the 13th of June at the PRL Workshop. You can also checkout the recorded talk.
Jun 1, 2022 Our survey on AutoRL has been published in the Journal of Artificial Intelligence Research.

selected publications

  1. Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration
    André Biedenkapp, Nguyen Dang, Martin S. Krejca, Frank Hutter, and Carola Doerr
    In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’22) 2022 Joint first authorship: André Biedenkapp & Nguyen Dang & Martin S. Krejca, Nominated for the best paper award
  2. 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, and Marius Lindauer
    Journal of Artificial Intelligence Research (JAIR) 2022
  3. TempoRL: Learning When to Act
    André Biedenkapp, Raghu Rajan, Frank Hutter, and Marius Lindauer
    In Proceedings of the 38th International Conference on Machine Learning (ICML 2021) 2021
  4. Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework
    André Biedenkapp, Furkan H Bozkurt, Theresa EimerFrank Hutter, and Marius Lindauer
    In Proceedings of the Twenty-fourth European Conference on Artificial Intelligence (ECAI’20) 2020