Uni-Logo

2021

  • Franke, Jörg K. H. and Köhler, Gregor and Biedenkapp, André and Hutter, Frank (arXiv)(bib)
    Sample-Efficient Automated Deep Reinforcement Learning
    In: International Conference on Learning Representations (ICLR) 2021
  • Müller, Samuel and Biedenkapp, André and Hutter, Frank (pdf)(bib)
    In-Loop Meta-Learning with Gradient-Alignment Reward
    In: AAAI workshop on Meta-Learning Challenges

2020

  • Awad, Noor and Shala, Gresa and Deng, Difan and Mallik, Neeratyoy and Feurer, Matthias and Eggensperger, Katharina and Biedenkapp, André and Vermetten, Diederick and Wang, Hao and Doerr, Carola and Lindauer, Marius and Hutter, Frank (arXiv)(bib)
    Squirrel: A Switching Hyperparameter Optimizer Description of the entry by AutoML.org & IOHprofiler to the NeurIPS 2020 BBO challenge
    In: arXiv:2012.08180 [cs.LG] (2020)
    Optimizer description for the NeurIPS 2020 BBO competition.
    Squirrel won the competition´s warm-starting friendly leaderboard.
    (Video presentation)(Code)
  • Rajan, Raghu and Diaz, Jessica Lizeth Borja and Guttikonda, Suresh and Ferreira, Fabio and Biedenkapp, André and Hutter, Frank (arXiv)(bib)
    MDP Playground: Controlling Dimensions of Hardness in Reinforcement Learning
    In: arXiv:1909.07750v3 [cs.LG] (2020)
  • Speck, David and Biedenkapp, André and Hutter, Frank and Mattmüller, Robert and Lindauer, Marius (arXiv)(bib)
    Learning Heuristic Selection with Dynamic Algorithm Configuration
    In: Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL@ICAPS'20)
    (Video presentation)(Code)(Project Page)
  • Shala, Gresa and Biedenkapp, André and Awad, Noor and Adriaensen, Steven and Lindauer, Marius and Hutter, Frank (pdf)(poster)(bib)
    Learning Step-Size Adaptation in CMA-ES
    In: Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN'20)
    (Video presentation)(Code)(Project Page)
  • Eimer, Theresa and Biedenkapp, André and Hutter, Frank and Lindauer, Marius (pdf)(bib)
    Towards Self-Paced Context Evaluations for Contextual Reinforcement Learning
    In: Workshop on Inductive Biases, Invariances and Generalization in RL (BIG@ICML'20)
    (Video presentation)(Code)
  • Biedenkapp, André and Rajan, Raghu and Hutter, Frank and Lindauer, Marius (pdf)(slides)(bib)
    Towards TempoRL: Learning When to Act
    In: Workshop on Inductive Biases, Invariances and Generalization in RL (BIG@ICML'20)
    (Video presentation)(Code)
  • Biedenkapp, André and Bozkurt, H. Furkan and Eimer, Theresa and Hutter, Frank and Lindauer, Marius (published)(pdf)(bib)
    Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework
    In: Proceedings of the Twenty-fourth European Conference on Artificial Intelligence (ECAI'20)
    (Video presentation)(Code)(Project Page)

2019

  • Lindauer, Marius and Feurer, Matthias and Eggensperger, Katharina and Biedenkapp, André and Hutter, Frank (arXiv)(slides)(bib)
    Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters
    In: IJCAI 2019 DSO Workshop
  • Biedenkapp, André and Bozkurt, H. Furkan and Hutter, Frank and Lindauer, Marius (arXiv)(pdf)(bib)
    Towards White-box Benchmarks for Algorithm Control
    In: IJCAI 2019 DSO Workshop
    (Project Page)
  • Lindauer, Marius and Eggensperger, Katharina and Feurer, Matthias and Biedenkapp, André and Marben, Joshua and Müller, Philipp and Hutter, Frank (arXiv)(bib)
    BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters
    In: arXiv:1908.06756 [cs.LG] (2019)
    (Code)(Project Page)

2018

  • Biedenkapp, André and Marben, Joshua and Lindauer, Marius and Hutter, Frank (pdf)(slides)(bib)
    CAVE: Configuration Assessment, Visualization and Evaluation
    In: Proceedings of the International Conference on Learning and Intelligent Optimization (LION'18)
    (Video presentation)(Code)(Project Page)

2017

  • Biedenkapp, André and Lindauer, Marius and Eggensperger, Katharina and Fawcett, Chris and Hoos, Holger H. and Hutter, Frank (pdf)(poster)(bib)
    Efficient Parameter Importance Analysis via Ablation with Surrogates
    In: Proceedings of the Thirty-First Conference on Artificial Intelligence (AAAI'17)
    (Code)(Project Page)