Marius Lindauer

2.6k total citations · 1 hit paper
35 papers, 939 citations indexed

About

Marius Lindauer is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, Marius Lindauer has authored 35 papers receiving a total of 939 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 8 papers in Computational Theory and Mathematics and 3 papers in Computer Networks and Communications. Recurrent topics in Marius Lindauer's work include Machine Learning and Data Classification (18 papers), Machine Learning and Algorithms (11 papers) and Advanced Multi-Objective Optimization Algorithms (7 papers). Marius Lindauer is often cited by papers focused on Machine Learning and Data Classification (18 papers), Machine Learning and Algorithms (11 papers) and Advanced Multi-Objective Optimization Algorithms (7 papers). Marius Lindauer collaborates with scholars based in Germany, Canada and United States. Marius Lindauer's co-authors include Frank Hutter, Holger H. Hoos, Bernd Bischl, Torsten Schaub, Difan Deng, Marc Becker, Michel Lang, Theresa Ullmann, Anne‐Laure Boulesteix and Tobias Pielok and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Trends in Plant Science and Artificial Intelligence.

In The Last Decade

Marius Lindauer

33 papers receiving 912 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges

2023 398 citations Bernd Bischl, Martin Binder et al. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Marius Lindauer Germany	12	492	149	125	82	77	35	939
Matthias Feurer Germany	6	651 1.3×	123 0.8×	66 0.5×	46 0.6×	70 0.9×	9	1.0k
Halina Kwaśnicka Poland	11	406 0.8×	165 1.1×	109 0.9×	63 0.8×	61 0.8×	55	1.0k
Mario Andrés Muñoz Australia	15	477 1.0×	265 1.8×	73 0.6×	91 1.1×	63 0.8×	55	857
Itziar Landa-Torres Spain	14	323 0.7×	136 0.9×	134 1.1×	118 1.4×	46 0.6×	28	829
Cătălin Stoean Romania	19	602 1.2×	120 0.8×	58 0.5×	36 0.4×	92 1.2×	75	1.1k
Waleed M. Mohamed Egypt	13	542 1.1×	230 1.5×	56 0.4×	44 0.5×	78 1.0×	23	931
Aaron Klein Germany	8	753 1.5×	129 0.9×	71 0.6×	39 0.5×	76 1.0×	25	1.2k
P Kumar India	11	161 0.3×	86 0.6×	102 0.8×	134 1.6×	52 0.7×	35	764
Zhuang Miao China	14	285 0.6×	152 1.0×	80 0.6×	65 0.8×	57 0.7×	110	878

Countries citing papers authored by Marius Lindauer

Since Specialization

Citations

This map shows the geographic impact of Marius Lindauer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Marius Lindauer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marius Lindauer more than expected).

Fields of papers citing papers by Marius Lindauer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marius Lindauer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Marius Lindauer. The network helps show where Marius Lindauer may publish in the future.

Co-authorship network of co-authors of Marius Lindauer

This figure shows the co-authorship network connecting the top 25 collaborators of Marius Lindauer. A scholar is included among the top collaborators of Marius Lindauer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Marius Lindauer. Marius Lindauer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Ortega, Antonio, et al.. (2025). AutoML for multi-class anomaly compensation of sensor drift. Measurement. 250. 117097–117097. 2 indexed citations

Bossek, Jakob, et al.. (2025). MO-SMAC: Multiobjective Sequential Model-Based Algorithm Configuration. Evolutionary Computation. 34(1). 29–52. 1 indexed citations

Bergman, Edward M., Matthias Feurer, Lennart Purucker, et al.. (2024). AMLTK: A Modular AutoML Toolkit in Python. The Journal of Open Source Software. 9(100). 6367–6367. 1 indexed citations

Lindauer, Marius, et al.. (2024). Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 38(11). 12172–12180. 1 indexed citations

Eftimov, Tome, et al.. (2024). Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 563–566. 1 indexed citations

Lindauer, Marius, et al.. (2024). Structure in Deep Reinforcement Learning: A Survey and Open Problems. Journal of Artificial Intelligence Research. 79. 1167–1236. 3 indexed citations

Denkena, Berend, et al.. (2023). Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools. The International Journal of Advanced Manufacturing Technology. 127(3-4). 1143–1164. 5 indexed citations

Lindauer, Marius, et al.. (2023). AutoML in heavily constrained applications. The VLDB Journal. 33(4). 957–979. 3 indexed citations

Bischl, Bernd, Martin Binder, Michel Lang, et al.. (2023). Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 13(2). 398 indexed citations breakdown →

10.

Raponi, Elena, et al.. (2023). Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. 483–486. 1 indexed citations

11.

Sharma, Neelesh, et al.. (2023). AutoML: advanced tool for mining multivariate plant traits. Trends in Plant Science. 28(12). 1451–1452.

12.

Biedenkapp, André, et al.. (2021). Self-Paced Context Evaluation for Contextual Reinforcement Learning. arXiv (Cornell University). 2 indexed citations

13.

Lindauer, Marius & Frank Hutter. (2020). Best Practices for Scientific Research on Neural Architecture Search. Journal of Machine Learning Research. 21(243). 1–18. 2 indexed citations

14.

Lindauer, Marius, Jan N. van Rijn, & Lars Kotthoff. (2019). The algorithm selection competitions 2015 and 2017. Artificial Intelligence. 272. 86–100. 24 indexed citations

15.

Eggensperger, Katharina, Marius Lindauer, Holger H. Hoos, Frank Hutter, & Kevin Leyton‐Brown. (2017). Efficient benchmarking of algorithm configurators via model-based surrogates. Machine Learning. 107(1). 15–41. 20 indexed citations

16.

Lindauer, Marius, Frank Hutter, Holger H. Hoos, & Torsten Schaub. (2017). AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract). Leiden Repository (Leiden University). 5025–5029. 3 indexed citations

17.

Bischl, Bernd, Pascal Kerschke, Lars Kotthoff, et al.. (2016). ASlib: A benchmark library for algorithm selection. Artificial Intelligence. 237. 41–58. 115 indexed citations

18.

Lindauer, Marius, Holger H. Hoos, Kevin Leyton‐Brown, & Torsten Schaub. (2016). Automatic construction of parallel portfolios via algorithm configuration. Artificial Intelligence. 244. 272–290. 17 indexed citations

19.

Lindauer, Marius, Holger H. Hoos, Frank Hutter, & Torsten Schaub. (2015). An automatically configured algorithm selector. publish.UP (University of Potsdam). 1 indexed citations

20.

Lindauer, Marius, Holger H. Hoos, Frank Hutter, & Torsten Schaub. (2015). AutoFolio: Algorithm Configuration for Algorithm Selection.. National Conference on Artificial Intelligence. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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