Carolin Benjamins, Anja Jankovic, Elena Raponi, Koen van der Blom, Marius Lindauer, Carola Doerr (2022): Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis,  6th Workshop on Meta-Learning at NeurIPS 2022, New Orleans

Neeratyoy Mallik, Carl Hvarfner, Danny Stoll, Maciej Janowski, Eddie Bergman, Marius Lindauer, Luigi Nardi, Frank Hutter (2022): PriorBand: HyperBand + Human Expert Knowledge,  2022 NeurIPS Workshop on Meta Learning (MetaLearn)

René Sass, Eddie Bergman, André Biedenkapp, Frank Hutter, Marius Lindauer (2022): DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning,  ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML)
arXiv: 2206.03493

Aditya Mohan, Tim Ruhkopf, Marius Lindauer (2022): Towards Meta-learned Algorithm Selection using Implicit Fidelity Information,  ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML)
arXiv: 2206.03130

Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter (2022): Automated Dynamic Algorithm Configuration,  Computing Research Repository (CoRR) (2022)
DOI: https://doi.org/10.48550/arXiv.2205.13881
arXiv: 2205.13881

Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer (2022): POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning,  CoRR
arXiv: arXiv:2205.11357

Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer (2022): Efficient Automated Deep Learning for Time Series Forecasting,  Proceedings of the European Conference on Machine Learning (ECML)
arXiv: 2205.05511

Carl Hvarfner, Danny Stoll, Artur Souza, Marius Lindauer, Frank Hutter, Luigi Nardi (2022): piBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization,  10th International Conference on Learning Representations, ICLR'22
arXiv: https://openreview.net/forum?id=MMAeCXIa89

Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer (2022): Contextualize Me - The Case for Context in Reinforcement Learning,  ArXiv Preprint
arXiv: https://arxiv.org/abs/2202.04500

Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter (2022): SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization,  Journal of Machine Learning Research (JMLR) -- MLOSS, Vol. 23, No. 54, pp. 1-9 Weitere Informationen

Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer (2022): Automated Reinforcement Learning (AutoRL): A Survey and Open Problems,  Journal of Artificial Intelligence Research (JAIR)
arXiv: 2201.03916

Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka (2021): Well-tuned Simple Nets Excel on Tabular Datasets,  Advances in Neural Information Processing Systems (NeurIPS 2021)
arXiv: 2106.11189

Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl (2021): Explaining Hyperparameter Optimization via Partial Dependence Plots,  Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) | Datei |
arXiv: 2111.04820

Theresa Eimer, Carolin Benjamins, Marius Lindauer (2021): Hyperparameters in Contextual RL are Highly Situational,  International Workshop on Ecological Theory of RL (at NeurIPS) | Datei |

Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka (2021): Well-tuned Simple Nets Excel on Tabular Datasets,  NeurIPS 2021
DOI: https://doi.org/10.48550/arXiv.2106.11189
arXiv: 2106.11189

Difan Deng, Marius Lindauer (2021): Searching in the Forest for Local Bayesian Optimization,  ECMLPKDD Workshop on Meta-Knowledge Transfe
arXiv: 2111.05834

Lukas Stürenburg, Berend Denkena, Marius Lindauer, Marcel Wichmann (2021): Maschinelles Lernen in der Prozessplanung

Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer (2021): CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning,  Workshop on Ecological Theory of Reinforcement Learning, NeurIPS 2021
arXiv: 2110.02102

Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor Awad, Marius Lindauer, Frank Hutter (2021): HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO,  Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track)
arXiv: 2109.06716

Artur Souza, Luigi Nardi, Leonardo B Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter (2021): Bayesian Optimization with a Prior for the Optimum,  Proceedings of the European conference on machine learning (ECML)
arXiv: 2006.14608

Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl (2022): Developing Open Source Educational Resources for Machine Learning and Data Science,  Teaching Machine Learning Workshop at ECML 2022
arXiv: 2107.14330

Bernd Bischl, Martin Binder, Michel Lang, Tobias Pielok, Jakob Richter, Stefan Coors, Janek Thomas, Theresa Ullmann, Marc Becker, Anne-Laure Boulesteix, Difan Deng, Marius Lindauer (2021): Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges,  WIREs Data Mining and Knowledge Discovery
DOI: 10.1002/widm.1484
arXiv: 2107.05847

Frederik Schubert, Theresa Eimer, Bodo Rosenhahn, Marius Lindauer (2021): Automatic Risk Adaptation in Distributional Reinforcement Learning
arXiv: 2106.06317

Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer (2021): Self-Paced Context Evaluation for Contextual Reinforcement Learning,  Proceedings of the international conference on machine learning (ICML)
arXiv: 2106.05110

André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer (2021): TempoRL: Learning When to Act,  Proceedings of the international conference on machine learning (ICML) 2021
arXiv: 2106.05262

Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl (2021): Towards Explaining Hyperparameter Optimization via Partial Dependence Plots,  8th ICML Workshop on Automated Machine Learning (AutoML) | Datei |

David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer (2021): Learning Heuristic Selection with Dynamic Algorithm Configuration,  International Conference on Automated Planning and Scheduling (ICAPS) 2021 | Datei |
arXiv: 2006.08246

Sergio Izquierdo, Julia Guerrero-Viu, Sven Hauns, Guilherme Miotto, Simon Schrodi, André Biedenkapp, Thomas Elsken, Difan Deng, Marius Lindauer, Frank Hutter (2021): Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization,  ICML 2021 Workshop AutoML
arXiv: 2105.01015

Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer (2021): DACBench: A Benchmark Library for Dynamic Algorithm Configuration,  Proceedings of the international joint conference on AI (IJCAI) 2021
arXiv: 2105.08541

Zhengying Liu, Adrien Pavao, Zhen Xu, Sergio Escalera, Fabio Ferreira, Isabelle Guyon, Sirui Hong, Frank Hutter, Rongrong Ji, Julio CS Jacques Junior, Ge Li, Marius Lindauer, Zhipeng Luo, Meysam Madadi, Thomas Nierhoff, Kangning Niu, Chunguang Pan, Danny Stoll, Sebastien Treguer, Jin Wang, Peng Wang, Chenglin Wu, Youcheng Xiong, Arbër Zela, Yang Zhang (2021): Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019 | Datei |

Lucas Zimmer, Marius Lindauer, Frank Hutter (2021): Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL.,  IEEE Transactions on Pattern Analysis and Machine Intelligence
arXiv: 2006.13799

Noor Awad, Gresa Shala, Difan Deng, Neeratyoy Mallik, Matthias Feurer, Katharina Eggensperger, Andre' Biedenkapp, Diederick Vermetten, Hao Wang, Carola Doerr, Marius Lindauer, Frank Hutter (2020): Squirrel: A Switching Hyperparameter Optimize
arXiv: 2012.08180

Berend Denkena, Marc-André Dittrich, Marius Lindauer, Julia Mainka, Lukas Stürenburg (2020): Using AutoML to Optimize Shape Error Prediction in Milling Processes,  Proceedings of the Machining Innovations Conference (MIC) | Datei |

Marius Lindauer, Frank Hutter (2019): Best Practices for Scientific Research on Neural Architecture Search,  Computing Research Repository (CoRR) (2019)
DOI: https://doi.org/10.48550/arXiv.1908.06756
arXiv: 1908.06756

Katharina Eggensperger, Kai Haase, Philipp Müller, Marius Lindauer, Frank Hutter (2020): Neural Model-based Optimization with Right-Censored Observations
arXiv: 2009.13828

Artur L. F. Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter (2020): Prior-guided Bayesian Optimization,  Computing Research Repository (CoRR) (2020) | Datei |

Gresa Shala, André Biedenkapp, Noor Awad, Steven Adriaensen, Marius Lindauer, Frank Hutter (2020): Learning Step-Size Adaptation in CMA-ES,  Proceedings of international PPSN conference 2020 | Datei |

André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer (2020): Towards TempoRL: Learning When to Act,  International Conference on Machine Learning (ICML) 2020 | Datei |

Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer (2020): Towards Self-Paced Context Evaluation for Contextual Reinforcement Learning,  International Conference on Machine Learning (ICML) 2020 | Datei |

Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter (2022): Auto-Sklearn 2.0: The Next Generation,  Journal of Machine Learning Research (JMLR), 23(261):1−61 Weitere Informationen
arXiv: 2007.04074

André Biedenkapp, H Furkan Bozkurt, Theresa Eimer, Frank Hutter, Marius Lindauer (2020): Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework,  European Conference on AI (ECAI) 2020 | Datei |

Marius Lindauer, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Frank Hutter (2019): Towards Assessing the Impact of Bayesian Optimization’s Own Hyperparameters,  Computing Research Repository (CoRR) (2019)
DOI: https://doi.org/10.48550/arXiv.1908.06674
arXiv: 1908.06674

Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Joshua Marben, Philipp Müller, Frank Hutter (2019): BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters,  Computing Research Repository (CoRR) (2019)
DOI: https://doi.org/10.48550/arXiv.1908.06756
arXiv: 1908.06756

Marius Lindauer, Jan N. van Rijn, Lars Kotthoff (2019): The Algorithm Selection Competitions 2015 and 2017,  Artif. Intell. (2019)
arXiv: 1805.01214

Hector Mendoza, Aaron Klein, Matthias Feuer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart, Maximilian Dippel, Marius Lindauer, Frank Hutter (2019): Towards Automatically-Tuned Deep Neural Networks,  Automated Machine Learning
DOI: https://doi.org/10.1007/978-3-030-05318-5
ISBN: 978-3-030-05317-8
ISSN: 2520-131X

Andre Biedenkapp, Joshua Marben, Marius Lindauer, Frank Hutter (2018): CAVE: Configuration Assessment, Visualization and Evaluation,  Learning and Intelligent Optimization (LION) 2018 | Datei |

Katharina Eggensperger, Marius Lindauer, Frank Hutter (2018): Neural Networks for Predicting Algorithm Runtime Distributions,  International Joint Conference on Artificial Intelligence (IJCAI) 2018
DOI: https://doi.org/10.48550/arXiv.1709.07615
arXiv: 1709.07615

Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown (2018): Selection and Configuration of Parallel Portfolios,  Handbook of Parallel Constraint Reasoning 2018: 583-615 | Datei |
ISBN: 978-3-319-63515-6

Marius Lindauer, Frank Hutter (2017): Warmstarting of Model-based Algorithm Configuration,  Computing Research Repository (CoRR) (2017)
DOI: https://doi.org/10.48550/arXiv.1709.04636
arXiv: 1709.04636

Marius Lindauer, Jan N. van Rijn, Lars Kotthoff (2017): Open Algorithm Selection Challenge 2017 Setup and Scenarios,  Open Algorithm Selection Challenge 2017 Setup and Scenarios (OASC) 2017 | Datei |

Markus Wagner, Tobias Friedrich, Marius Lindauer (2017): Improving local search in a minimum vertex cover solver for classes of networks,  IEEE Congress on Evolutionary Computation (CEC) 2017
DOI: 10.1109/CEC.2017.7969507
ISBN: 978-1-5090-4602-7

Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown (2017): Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates,  Computing Research Repository (CoRR) (2017)
DOI: https://doi.org/10.48550/arXiv.1703.10342
arXiv: 1703.10342

Katharina Eggensperger, Marius Lindauer, Frank Hutter (2017): Pitfalls and Best Practices in Algorithm Configuration,  Computing Research Repository (CoRR) (2017)
DOI: https://doi.org/10.48550/arXiv.1705.06058
arXiv: 1705.06058

Marius Lindauer, Holger H. Hoos, Kevin Leyton-Brown, Torsten Schaub (2017): Automatic Construction of Parallel Portfolios via Algorithm Configuration,  Artif. Intell. (2017)
DOI: https://doi.org/10.1016/j.artint.2016.05.004

Andre Biedenkapp, Marius Lindauer, Katharina Eggensperger, Frank Hutter, Chris Fawcett, Holger H. Hoos (2017): Efficient Parameter Importance Analysis via Ablation with Surrogates,  Association for the Advancement of Artificial Intelligence (AAAI) 2017
DOI: https://doi.org/10.1609/aaai.v31i1.10657

Marius Lindauer, Rolf-David Bergdoll, Frank Hutter (2016): An Empirical Study of Per-Instance Algorithm Scheduling,  Learning and Intelligent Optimization (LION) 2016
DOI: https://doi.org/10.1007/978-3-319-50349-3_20
ISBN: 978-3-319-50348-6

Markus Wagner, Marius Lindauer, Mustafa Misir, Samadhi Nallaperuma, Frank Hutter (2016): A case study of algorithm selection for the traveling thief problem,  Computing Research Repository (CoRR) (2016)
arXiv: 1609.00462

Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos, Kevin Leyton-Brown (2016): The Configurable SAT Solver Challenge (CSSC),  Computing Research Repository (CoRR) (2015)
DOI: https://doi.org/10.48550/arXiv.1505.01221
arXiv: 1505.01221

Norbert Manthey, Marius Lindauer (2016): SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers,  SAT 2016
DOI: 10.1007/978-3-319-40970-2_36

Stefan Falkner, Marius Lindauer, Frank Hutter (2015): SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers,  SAT 2015 | Datei |

Marius Lindauer, Holger H. Hoos, Frank Hutter, Torsten Schaub (2015): AutoFolio: An Automatically Configured Algorithm Selector,  Journal of Artificial Intelligence Research (2015)
DOI: https://doi.org/10.1613/jair.4726

Marius Lindauer, Holger H. Hoos, Frank Hutter, Torsten Schaub: (2015): AutoFolio: Algorithm Configuration for Algorithm Selection,  Association for the Advancement of Artificial Intelligence (AAAI) Workshop: Algorithm Configuration 2015 | Datei |

Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren (2015): ASlib: A Benchmark Library for Algorithm Selection,  Computing Research Repository (CoRR) (2015)
DOI: https://doi.org/10.48550/arXiv.1506.02465
arXiv: 1506.02465

Marius Lindauer, Holger H. Hoos, Frank Hutter (2015): From Sequential Algorithm Selection to Parallel Portfolio Selection,  Learning and Intelligent Optimization (LION) 2015
DOI: 10.1007/978-3-319-19084-6_1

Marius Lindauer (2014): Algorithm Selection, Scheduling and Configuration of Boolean Constraint Solvers | Datei |

Holger Hoos, Marius Lindauer, Torsten Schaub (2014): claspfolio 2: Advances in Algorithm Selection for Answer Set Programming,  Theory and Practice of Logic Programming
DOI: 10.1017/S1471068414000210
arXiv: 1405.1520

Frank Hutter, Manuel López-Ibáñez, Chris Fawcett, Marius Lindauer, Holger Hoos, Kevin Leyton-Brown and Thomas Stützle (2014): AClib: a Benchmark Library for Algorithm Configuration,  Learning and Intelligent Optimization (LION 8)
DOI: https://doi.org/10.1007/978-3-319-09584-4
ISBN: 978-3-319-09583-7
ISSN: 0302-9743

Holger H. Hoos, Roland Kaminski, Marius Lindauer, Torsten Schaub (2013): aspeed: Solver Scheduling via Answer Set Programming,  Theory Pract. Log. Program. 15(1): 117-142 (2015)
DOI: 10.1017/S1471068414000015
arXiv: 1401.1024

Holger H. Hoos, Benjamin Kaufmann, Torsten Schaub, Marius Schneider (2013): Robust Benchmark Set Selection for Boolean Constraint Solvers,  Learning and Intelligent Optimization (LION) 2013
DOI: https://doi.org/10.1007/978-3-642-44973-4_16
ISBN: 978-3-642-44972-7

Holger H. Hoos, Roland Kaminski, Torsten Schaub, Marius Schneider (2012): aspeed: ASP-based Solver Scheduling,  International Conference on Logic Programming (ICLP (Technical Communications)) 2012 | Datei |
ISBN: 978-3-939897-43-9
ISSN: 1868-8969

Bryan Silverthorn, Yuliya Lierler, Marius Schneider (2012): Surviving Solver Sensitivity: An ASP Practitioner’s Guide,  International Conference on Logic Programming (ICLP (Technical Communications)) 2012
DOI: 10.4230/LIPIcs.ICLP.2012.164
ISBN: 978-3-939897-43-9
ISSN: 1868-8969

Holger Hoos, Kevin Leyton-Brown, Torsten Schaub, Marius Schneider (2012): Algorithm Configuration for Portfolio-based Parallel SAT-Solving,  Proceedings of the ECAI-12 Workshop on Combining Constraint Solving with MIning and Learning | Datei |

Marius Schneider, Holger H. Hoos (2012): Quantifying Homogeneity of Instance Sets for Algorithm Configuration,  Learning and Intelligent Optimization (LION) 2012
DOI: https://doi.org/10.1007/978-3-642-34413-8_14
ISBN: 978-3-642-34412-1

Martin Gebser, Roland Kaminski, Benjamin Kaufmann, Torsten Schaub, Marius Thomas Schneider, Stefan Ziller (2011): A Portfolio Solver for Answer Set Programming: Preliminary Report,  Logic Programming and Non-Monotonic Reasoning (LPNMR) 2011
DOI: https://doi.org/10.1007/978-3-642-20895-9_40
ISBN: 978-3-642-20894-2

Martin Gebser, Benjamin Kaufmann, Roland Kaminski, Max Ostrowski, Torsten Schaub, Marius Schneider (2011): Potassco: The Potsdam Answer Set Solving Collection,  AI Commun. (2011)
DOI: 10.3233/AIC-2011-0491

Maximilian Möller, Marius Schneider, Martin Wegner, Torsten Schaub (2010): Centurio, a General Game Player: Parallel, Java- and ASP-based,  Künstliche Intell. (2011)
DOI: https://doi.org/10.1007/s13218-010-0077-4

Benjamin Kaufmann, Torsten Schaub, Marius Schneider (2012): clasp, claspfolio, aspeed: Three Solvers from the Answer Set Solving Collection Potassco | Datei |

Martin Gebser, Holger Jost, Roland Kaminski, Philipp Obermeier, Orkunt Sabuncu, Torsten Schaub, Marius Schneider (2013): Ricochet Robots: A transverse ASP benchmark,  Logic Programming and Non-Monotonic Reasoning (LPNMR) 2013
DOI: https://doi.org/10.1007/978-3-642-40564-8_35
ISBN: 978-3-642-40563-1

Benjamins, Carolin and Raponi, Elena and Jankovic, Anja and van der Blom, Koen and Santoni, Maria Laura and Lindauer, Marius and Doerr, Carola (2022): PI is back! Switching Acquisition Functions in Bayesian Optimization,  2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems
arXiv: 2211.01455

Marcel Wever, Alexander Tornede, Felix Mohr, Eyke Hüllermeier (2021): AutoML for Multi-Label Classification: Overview and Empirical Evaluation,  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
DOI: 10.1109/tpami.2021.3051276

Alexander Tornede, Marcel Wever, Eyke Hüllermeier (2020): Extreme Algorithm Selection with Dyadic Feature Representation,  Discovery Science 2020
DOI: 10.1007/978-3-030-61527-7_21
arXiv: 2001.10741

Felix Mohr, Marcel Wever, Alexander Tornede, Eyke Hüllermeier (2021): Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning,  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
DOI: 10.1109/TPAMI.2021.3056950

Alexander Tornede, Marcel Wever, Stefan Werner, Felix Mohr, Eyke Hüllermeier (2020): Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis,  12th Asian Conference on Machine Learning (ACML)
DOI: 10.48550/arXiv.2007.02816
arXiv: 2007.02816

Alexander Tornede, Marcel Wever, Eyke Hüllermeier (2019): Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking,  29th Workshop Computational Intelligence

Jonas Hanselle, Alexander Tornede, Marcel Wever, Eyke Hüllermeier (2020): Hybrid Ranking and Regression for Algorithm Selection,  43rd German Conference on Artificial Intelligence (KI 2020)
DOI: 10.1007/978-3-030-58285-2_5

Tanja Tornede, Alexander Tornede, Marcel Wever, Felix Mohr, Eyke Hüllermeier (2021): AutoML for Predictive Maintenance: One Tool to RUL them all,  IoTStream @ ECMLPKDD 2020, 2020
DOI: 10.1007/978-3-030-66770-2_8

Marcel Wever, Felix Mohr, Alexander Tornede, Eyke Hüllermeier (2019): Automating Multi-Label Classification Extending ML-Plan,  6th ICML Workshop on Automated Machine Learning

Tanja Tornede, Alexander Tornede, Marcel Wever, Eyke Hüllermeier (2021): Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,  Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2021)
DOI: 10.1145/3449639.3459395

Alexander Tornede, Lukas Gehring, Tanja Tornede, Marcel Wever, Eyke Hüllermeier (2022): Algorithm selection on a meta level,  Machine Learning
DOI: 10.1007/s10994-022-06161-4

Tanja Tornede, Alexander Tornede, Jonas Hanselle, Marcel Wever, Felix Mohr, Eyke Hüllermeier (2021): Towards Green Automated Machine Learning: Status Quo and Future Directions,  ArXiv
arXiv: 2111.05850

Alexander Tornede, Marcel Wever, Eyke Hüllermeier (2020): Towards Meta-Algorithm Selection,  Workshop on Meta-Learning (MetaLearn 2020) @ NeurIPS 2020
arXiv: 2011.08784

Marcel Wever, Alexander Tornede, Felix Mohr, Eyke Hüllermeier (2020): LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification,  Symposium on Intelligent Data Analysis (IDA 2020)
DOI: 10.1007/978-3-030-44584-3_44

Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney (2022): A Survey of Methods for Automated Algorithm Configuration,  Journal of Artificial Intelligence Research
DOI: 10.1613/jair.1.13676

Eyke Hüllermeier, Felix Mohr, Alexander Tornede, Marcel Wever (2021): Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,  ECMLPKDD Workshop on Automating Data Science (ADS2021)
DOI: 2109.04744

Felix Mohr, Marcel Wever, Alexander Tornede, Eyke Hüllermeier (2019): From Automated to On-The-Fly Machine Learning,  INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik–Informatik für Gesellschaft
DOI: 10.18420/inf2019_40

Karina Gevers, Alexander Tornede, Marcel Wever, Volker Schöppner, Eyke Hüllermeier (2022): A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials,  Welding in the World
DOI: 10.1007/s40194-022-01339-9

Lukas Fehring, Jonas Hanselle, Alexander Tornede (2022): HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection,  Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022
arXiv: 2210.17341

Alexander Tornede, Viktor Bengs, Eyke Hüllermeier (2022): Machine Learning for Online Algorithm Selection under Censored Feedback,  Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI 2022)
DOI: 10.1609/aaai.v36i9.21279

Marius Lindauer, Frank Hutter, Holger H. Hoos, Torsten Schaub (2017): AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract),  International Joint Conference on Artificial Intelligence (IJCAI) 2017
DOI: https://doi.org/10.24963/ijcai.2017/715

Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter, (2018): Practical Automated Machine Learning for the AutoML Challenge 2018,  International Conference on Machine Learning (ICML) 2018 AutoML Workshop | Datei |

André Biedenkapp, H. Furkan Bozkurt, Frank Hutter, Marius Lindauer (2019): Towards White-box Benchmarks for Algorithm Control,  CoRR abs/1906.07644 (2019)
arXiv: 1906.07644

Lior Fuks, Noor H. Awad, Frank Hutter, Marius Lindauer (2019): An Evolution Strategy with Progressive Episode Lengths for Playing Games,  IJCAI 2019: 1234-1240
DOI: https://doi.org/10.24963/ijcai.2019/172

André Biedenkapp, H. Furkan Bozkurt, Theresa Eimer, Frank Hutter, Marius Lindauer (2020): Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework,  European Conference on Artificial Intelligence (ECAI) 2020
DOI: 10.3233/FAIA200122

André Biedenkapp, David Speck, Silvan Sievers, Frank Hutter, Marius Lindauer, Jendrik Seipp (2022): Learning Domain-Independent Policies for Open List Selection,  Proceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), pp. 1-9 | Datei |

Marius Lindauer, Lars Kotthoff (2017): What can we learn from algorithm selection data? (Breakout Session Report),  Dagstuhl Reports

Jonas Hanselle, Alexander Tornede, Marcel Wever, Eyke Hüllermeier (2021): Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data,  25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021)
DOI: 10.1007/978-3-030-75762-5_13