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2021

  • Eimer, Theresa and Biedenkapp, André and Reimer, Maximilian and Adriaensen, Steven and Hutter, Frank and Lindauer, Marius (pdf)(arXiv)(code)(bib)(project page)
    DACBench: A Benchmark Library for Dynamic Algorithm Configuration
    In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI'21)
  • Speck, David and Biedenkapp, André and Hutter, Frank and Mattmüller, Robert and Lindauer, Marius (published)(pdf)(arXiv)(code)(bib)(project page)
    Learning Heuristic Selection with Dynamic Algorithm Configuration
    In: Proceedings of the 31st International Conference on Automated Planning and Scheduling (ICAPS'21)
  • Izquierdo, Sergio and Guerrero-Viu, Julia and Hauns, Sven and Miotto, Guilherme and Schrodi, Simon and Biedenkapp, André and Elsken, Thomas and Deng, Difan and Lindauer, Marius and Hutter, Frank (pdf)(arXiv)(code)(poster)(bib)
    Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization
    In: Workshop on Automated Machine Learning (AutoML@ICML'21)
  • Eimer, Theresa and Biedenkapp, André and Hutter, Frank and Lindauer, Marius (published)(arXiv)(code)(poster)(bib)
    Self-Paced Context Evaluations for Contextual Reinforcement Learning
    In: Proceedings of the 38th International Conference on Machine Learning (ICML 2021)
  • Biedenkapp, André and Rajan, Raghu and Hutter, Frank and Lindauer, Marius (published)(pdf)(arXiv)(code)(supplementary)(poster)(bib)
    TempoRL: Learning When to Act
    In: Proceedings of the 38th International Conference on Machine Learning (ICML 2021)
  • Franke, Jörg K. H. and Köhler, Gregor and Biedenkapp, André and Hutter, Frank (published)(arXiv)(code)(bib)(blog post)(video presentation)
    Sample-Efficient Automated Deep Reinforcement Learning
    In: International Conference on Learning Representations (ICLR) 2021
  • Zhang, Baohe and Rajan, Raghu and Pineda, Luis and Lambert, Nathan and Biedenkapp, André and Chua, Kurtland and Hutter, Frank and Calandra, Roberto (published)(pdf)(arXiv)(code)(bib)(blog post)(video presentation)
    On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
    In: Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS)'21
  • Müller, Samuel and Biedenkapp, André and Hutter, Frank (pdf)(code)(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)(code)(bib)(video presentation)
    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.
  • 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)(code)(bib)(project page)(video presentation)
    Learning Heuristic Selection with Dynamic Algorithm Configuration
    In: Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL@ICAPS'20)
  • Shala, Gresa and Biedenkapp, André and Awad, Noor and Adriaensen, Steven and Lindauer, Marius and Hutter, Frank (pdf)(code)(poster)(bib)(project page)(blog post)(video presentation)
    Learning Step-Size Adaptation in CMA-ES
    In: Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN'20)
  • Eimer, Theresa and Biedenkapp, André and Hutter, Frank and Lindauer, Marius (pdf)(code)(bib)(video presentation)
    Towards Self-Paced Context Evaluations for Contextual Reinforcement Learning
    In: Workshop on Inductive Biases, Invariances and Generalization in RL (BIG@ICML'20)
  • Biedenkapp, André and Rajan, Raghu and Hutter, Frank and Lindauer, Marius (pdf)(code)(slides)(bib)(video presentation)
    Towards TempoRL: Learning When to Act
    In: Workshop on Inductive Biases, Invariances and Generalization in RL (BIG@ICML'20)
  • Biedenkapp, André and Bozkurt, H. Furkan and Eimer, Theresa and Hutter, Frank and Lindauer, Marius (published)(pdf)(code)(bib)(project page)(blog post)(video presentation)
    Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework
    In: Proceedings of the Twenty-fourth European Conference on Artificial Intelligence (ECAI'20)

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 (pdf)(arXiv)(bib)(project page)
    Towards White-box Benchmarks for Algorithm Control
    In: IJCAI 2019 DSO Workshop
  • Lindauer, Marius and Eggensperger, Katharina and Feurer, Matthias and Biedenkapp, André and Marben, Joshua and Müller, Philipp and Hutter, Frank (arXiv)(code)(bib)(project page)
    BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters
    In: arXiv:1908.06756 [cs.LG] (2019)

2018

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

2017

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