Marius Lindauer 个人学术档案

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i10 指数	65	61

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Frank HutterELLIS Institute & Professor, University of Freiburg, Germany在 cs.uni-freiburg.de 的电子邮件经过验证
André BiedenkappUniversity of Freiburg在 cs.uni-freiburg.de 的电子邮件经过验证
Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British Columbia在 cs.rwth-aachen.de 的电子邮件经过验证
Katharina EggenspergerEarly Career Group Leader "AutoML for Science" | University of Tübingen在 uni-tuebingen.de 的电子邮件经过验证
Torsten SchaubUniversity of Potsdam在 cs.uni-potsdam.de 的电子邮件经过验证
Matthias FeurerLMU Munich & Munich Center for Machine Learning在 stat.uni-muenchen.de 的电子邮件经过验证
Difan DengLeibniz Universität Hannover在 tnt.uni-hannover.de 的电子邮件经过验证
Theresa EimerDoctoral Researcher, Leibniz Universität Hannover在 ai.uni-hannover.de 的电子邮件经过验证
C. Benjamins在 tnt.uni-hannover.de 的电子邮件经过验证
Bernd BischlChair of Statistical Learning and Data Science, LMU Munich在 stat.uni-muenchen.de 的电子邮件经过验证
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI Chair在 cs.ubc.ca 的电子邮件经过验证
Roland KaminskiUniversity of Potsdam在 cs.uni-potsdam.de 的电子邮件经过验证
Stefan FalknerBosch Corporate Research在 de.bosch.com 的电子邮件经过验证
Martin GebserUniversity of Klagenfurt / Graz University of Technology在 aau.at 的电子邮件经过验证
Lars KotthoffUniversity of Wyoming在 uwyo.edu 的电子邮件经过验证
Aditya MohanPhD Researcher at Leibniz Universität Hannover在 ai.uni-hannover.de 的电子邮件经过验证
Noor AwadPostdoctoral Research Fellow, University of Freiburg在 cs.uni-freiburg.de 的电子邮件经过验证
Tim RuhkopfPhD Candidate, Leibniz University Hannover在 ai.uni-hannover.de 的电子邮件经过验证
Raghu RajanUniversity of Freiburg在 cs.uni-freiburg.de 的电子邮件经过验证
Luigi NardiAssociate Professor in Machine Learning at Lund University; Research Staff at Stanford University在 cs.lth.se 的电子邮件经过验证

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Marius Lindauer

其他姓名Marius Schneider

Leibniz University Hannover (Germany), Institute of Artificial Intelligence LUH|AI, L3S Research

在 ai.uni-hannover.de 的电子邮件经过验证 - 首页

Machine Learning AutoML Reinforcement Learning Interpretable Machine Learning Artificial Intelligence


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	621	2011
Auto-sklearn 2.0: Hands-free automl via meta-learning M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter Journal of Machine Learning Research (JMLR), 2022	393*	2022
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges B Bischl, M Binder, M Lang, T Pielok, J Richter, S Coors, J Thomas, ... Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 13 (2 …, 2023	331	2023
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. M Lindauer, K Eggensperger, M Feurer, A Biedenkapp, D Deng, ... J. Mach. Learn. Res. 23, 54:1-54:9, 2022	285	2022
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	276	2016
Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. L Zimmer, M Lindauer, F Hutter IEEE Transactions on Pattern Analysis and Machine Intelligence 43 (9), 3079 …, 2021	232	2021
Well-tuned Simple Nets Excel on Tabular Datasets A Kadra, M Lindauer, F Hutter, J Grabocka Advances in Neural Information Processing Systems 34, 2021	206*	2021
AutoFolio: an automatically configured algorithm selector M Lindauer, HH Hoos, F Hutter, T Schaub Journal of Artificial Intelligence Research 53, 745-778, 2015	162	2015
Best practices for scientific research on neural architecture search M Lindauer, F Hutter Journal of Machine Learning Research 21 (243), 1-18, 2020	147	2020
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	108	2018
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	103	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	99	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	96	2014
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	90	2015
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO K Eggensperger, P Müller, N Mallik, M Feurer, R Sass, A Klein, N Awad, ... NeurIPS Track Datasets and Benchmarks, 2021	84	2021
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	80	2017
Warmstarting of model-based algorithm configuration M Lindauer, F Hutter Thirty-Second AAAI Conference on Artificial Intelligence, 2018	78	2018
Automated reinforcement learning (autorl): A survey and open problems J Parker-Holder, R Rajan, X Song, A Biedenkapp, Y Miao, T Eimer, ... Journal of Artificial Intelligence Research 74, 517-568, 2022	77	2022
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	77	2017
Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework A Biedenkapp, HF Bozkurt, T Eimer, F Hutter, M Lindauer European Conference on AI (ECAI), 2020	71	2020

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