InstitutTeam
Theresa Eimer
Theresa Eimer
Adresse
Appelstraße 9a
30167 Hannover
Gebäude
Raum
Theresa Eimer
Adresse
Appelstraße 9a
30167 Hannover
Gebäude
Raum

Research Interests

  • Generalization in Reinforcement Learning
  • Dynamic Algorithm Configuration
  • Automated Reinforcement Learning
  • Meta Reinforcement Learning
  • Societal Impact of Machine Learning

Curriculum Vitae

  • Education

    2022: Chair for Diversity & Inclusion at the AutoML-Conf

    since 2020: Doctoral Researcher at the Leibniz University Hannover

    2016 - 2019: M.Sc. Computer Science at the Albert-Ludwigs University Freiburg
    Thesis: Improved Meta-Learning for Dynamic Algorithm Configuration
    Supervisor: Prof. Dr. Frank Hutter

    2013 - 2016: B.Sc. Computer Science at the University of Hamburg
    Thesis: On Thue Numbers
    Supervisor: Dr. Frank Heitmann

Publications

  • Carolin Benjamins, Anja Jankovic, Elena Raponi, Koen van der Blom, Marius Lindauer, Carola Doerr (2022): Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis6th 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 Knowledge2022 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 LearningICML 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 InformationICML 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 ConfigurationComputing 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 LearningCoRR
    arXiv: arXiv:2205.11357
  • Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer (2022): Efficient Automated Deep Learning for Time Series ForecastingProceedings 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 Optimization10th 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 LearningArXiv 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 OptimizationJournal 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 ProblemsJournal of Artificial Intelligence Research (JAIR)
    arXiv: 2201.03916
  • Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka (2021): Well-tuned Simple Nets Excel on Tabular DatasetsAdvances 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 PlotsProceedings 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 SituationalInternational 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 DatasetsNeurIPS 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 OptimizationECMLPKDD 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 LearningWorkshop 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 HPOProceedings 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 OptimumProceedings 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 ScienceTeaching 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 ChallengesWIREs 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 LearningProceedings of the international conference on machine learning (ICML)
    arXiv: 2106.05110
  • André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer (2021): TempoRL: Learning When to ActProceedings 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 Plots8th 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 ConfigurationInternational 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 OptimizationICML 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 ConfigurationProceedings 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 ProcessesProceedings of the Machining Innovations Conference (MIC) | Datei |
  • Marius Lindauer, Frank Hutter (2019): Best Practices for Scientific Research on Neural Architecture SearchComputing 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 OptimizationComputing Research Repository (CoRR) (2020) | Datei |
  • Gresa Shala, André Biedenkapp, Noor Awad, Steven Adriaensen, Marius Lindauer, Frank Hutter (2020): Learning Step-Size Adaptation in CMA-ESProceedings of international PPSN conference 2020 | Datei |
  • André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer (2020): Towards TempoRL: Learning When to ActInternational Conference on Machine Learning (ICML) 2020 | Datei |
  • Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer (2020): Towards Self-Paced Context Evaluation for Contextual Reinforcement LearningInternational Conference on Machine Learning (ICML) 2020 | Datei |
  • Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter (2022): Auto-Sklearn 2.0: The Next GenerationJournal 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 FrameworkEuropean 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 HyperparametersComputing 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 HyperparametersComputing 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 2017Artif. 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 NetworksAutomated 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 EvaluationLearning and Intelligent Optimization (LION) 2018 | Datei |
  • Katharina Eggensperger, Marius Lindauer, Frank Hutter (2018): Neural Networks for Predicting Algorithm Runtime DistributionsInternational 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 PortfoliosHandbook of Parallel Constraint Reasoning 2018: 583-615 | Datei |
    ISBN: 978-3-319-63515-6
  • Marius Lindauer, Frank Hutter (2017): Warmstarting of Model-based Algorithm ConfigurationComputing 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 ScenariosOpen 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 networksIEEE 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 SurrogatesComputing 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 ConfigurationComputing 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 ConfigurationArtif. 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 SurrogatesAssociation 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 SchedulingLearning 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 problemComputing 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 SolversSAT 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 SolversSAT 2015 | Datei |
  • Marius Lindauer, Holger H. Hoos, Frank Hutter, Torsten Schaub (2015): AutoFolio: An Automatically Configured Algorithm SelectorJournal 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 SelectionAssociation 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 SelectionComputing 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 SelectionLearning 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 ProgrammingTheory 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 ConfigurationLearning 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 ProgrammingTheory 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 SolversLearning 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 SchedulingInternational 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 GuideInternational 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-SolvingProceedings 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 ConfigurationLearning 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 ReportLogic 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 CollectionAI 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-basedKünstliche 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 benchmarkLogic 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 Optimization2022 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 EvaluationIEEE 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 RepresentationDiscovery 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 LearningIEEE 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 Analysis12th 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 Ranking29th Workshop Computational Intelligence
  • Jonas Hanselle, Alexander Tornede, Marcel Wever, Eyke Hüllermeier (2020): Hybrid Ranking and Regression for Algorithm Selection43rd 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 allIoTStream @ 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-Plan6th 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 MaintenanceProceedings 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 levelMachine 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 DirectionsArXiv
    arXiv: 2111.05850
  • Alexander Tornede, Marcel Wever, Eyke Hüllermeier (2020): Towards Meta-Algorithm SelectionWorkshop 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 ClassificationSymposium 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 ConfigurationJournal 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 MetareasoningECMLPKDD 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 LearningINFORMATIK 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 materialsWelding 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 SelectionWorkshop 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 FeedbackProceedings 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 2018International 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 ControlCoRR 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 GamesIJCAI 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 FrameworkEuropean 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 SelectionProceedings 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 Data25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021)
    DOI: 10.1007/978-3-030-75762-5_13

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