Marius Lindauer

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Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
Torsten SchaubUniversity of PotsdamVerified email at cs.uni-potsdam.de
Holger HoosLeiden University, The Netherlands & University of British Columbia, CanadaVerified email at liacs.nl
André BiedenkappPhD candidate, University of FreiburgVerified email at cs.uni-freiburg.de
Katharina EggenspergerPhD Student, Machine Learning Lab, University of FreiburgVerified email at informatik.uni-freiburg.de
Roland KaminskiUniversity of PotsdamVerified email at cs.uni-potsdam.de
Matthias FeurerMachine Learning group, University of FreiburgVerified email at informatik.uni-freiburg.de
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairVerified email at cs.ubc.ca
Martin GebserUniversity of Klagenfurt / Graz University of TechnologyVerified email at aau.at
Stefan FalknerBosch Center for Artificial IntelligenceVerified email at de.bosch.com
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Theresa EimerDoctoral Researcher, Leibniz Universität HannoverVerified email at tnt.uni-hannover.de
Chris FawcettPhD Student, University of British ColumbiaVerified email at cs.ubc.ca
Yuri MalitskyIBM ResearchVerified email at us.ibm.com
Kevin TierneyBielefeld UniversityVerified email at uni-bielefeld.de
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Pascal KerschkeProfessor of Big Data Analytics in Transportation, TU Dresden, GermanyVerified email at tu-dresden.de
Alexandre FréchettePhD Student, University of British ColumbiaVerified email at cs.ubc.ca
Jan N. van RijnLeiden UniversityVerified email at liacs.leidenuniv.nl

Marius Lindauer

Leibniz University Hannover (Germany)

Verified email at tnt.uni-hannover.de - Homepage

Machine Learning AutoML Reinforcement Learning Explainable Machine Learning Artificial Intelligence


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Potassco: The Potsdam answer set solving collection M Gebser, B Kaufmann, R Kaminski, M Ostrowski, T Schaub, M Schneider AI Communications 24 (2), 107-124, 2011	523	2011
Aslib: A benchmark library for algorithm selection B Bischl, P Kerschke, L Kotthoff, M Lindauer, Y Malitsky, A Fréchette, ... Artificial Intelligence 237, 41-58, 2016	174	2016
AutoFolio: an automatically configured algorithm selector M Lindauer, HH Hoos, F Hutter, T Schaub Journal of Artificial Intelligence Research 53, 745-778, 2015	98	2015
A portfolio solver for answer set programming: Preliminary report M Gebser, R Kaminski, B Kaufmann, T Schaub, MT Schneider, S Ziller International Conference on Logic Programming and Nonmonotonic Reasoning …, 2011	87	2011
claspfolio 2: Advances in Algorithm Selection for Answer Set Programming H Hoos, M Lindauer, T Schaub Theory and Practice of Logic Programming 14 (Special Issue 4-5), 569--585, 2014	72	2014
AClib: A benchmark library for algorithm configuration F Hutter, M López-Ibánez, C Fawcett, M Lindauer, HH Hoos, ... International Conference on Learning and Intelligent Optimization, 36-40, 2014	67	2014
Practical automated machine learning for the automl challenge 2018 M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter International Workshop on Automatic Machine Learning at ICML, 1189-1232, 2018	62	2018
A case study of algorithm selection for the traveling thief problem M Wagner, M Lindauer, M Mısır, S Nallaperuma, F Hutter Journal of Heuristics, 1-26, 2017	60	2017
The configurable SAT solver challenge (CSSC) F Hutter, M Lindauer, A Balint, S Bayless, H Hoos, K Leyton-Brown Artificial Intelligence 243, 1-25, 2017	59	2017
Potassco User Guide M Gebser, R Kaminski, B Kaufmann, M Lindauer, M Ostrowski, J Romero, ... Institute for Informatics, University of Potsdam, second edition edition, 2015	58*	2015
Best practices for scientific research on neural architecture search M Lindauer, F Hutter Journal of Machine Learning Research 21 (243), 1-18, 2020	55	2020
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization M Lindauer, K Eggensperger, M Feurer, A Biedenkapp, D Deng, ... arXiv preprint arXiv:2109.09831, 2021	54*	2021
Towards Automatically-Tuned Deep Neural Networks H Mendoza, A Klein, M Feurer, JT Springenberg, M Urban, M Burkart, ... Automated Machine Learning, 135-149, 2019	46	2019
Warmstarting of model-based algorithm configuration M Lindauer, F Hutter Thirty-Second AAAI Conference on Artificial Intelligence, 2018	46	2018
aspeed: Solver scheduling via answer set programming H HOOS, R KAMINSKI, M LINDAUER, T SCHAUB Theory and Practice of Logic Programming, 1-26, 2014	46*	2014
Efficient benchmarking of algorithm configurators via model-based surrogates K Eggensperger, M Lindauer, HH Hoos, F Hutter, K Leyton-Brown Machine Learning, 1-27, 2018	43	2018
Centurio, a general game player: Parallel, Java-and ASP-based M Möller, M Schneider, M Wegner, T Schaub KI-Künstliche Intelligenz 25 (1), 17-24, 2011	38	2011
Auto-Sklearn 2.0: The Next Generation M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter arXiv preprint arXiv:2007.04074, 2020	35	2020
From Sequential Algorithm Selection to Parallel Portfolio Selection M Lindauer, H Hoos, F Hutter Proceedings of international Conference on Learning and Intelligent …, 2015	33	2015
The algorithm selection competitions 2015 and 2017 M Lindauer, JN van Rijn, L Kotthoff Artificial Intelligence 272, 86-100, 2019	30*	2019

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