Bernd Bischl

9.5k total citations · 3 hit papers
112 papers, 4.0k citations indexed

About

Bernd Bischl is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Signal Processing. According to data from OpenAlex, Bernd Bischl has authored 112 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Artificial Intelligence, 18 papers in Computational Theory and Mathematics and 15 papers in Signal Processing. Recurrent topics in Bernd Bischl's work include Machine Learning and Data Classification (37 papers), Advanced Multi-Objective Optimization Algorithms (18 papers) and Explainable Artificial Intelligence (XAI) (13 papers). Bernd Bischl is often cited by papers focused on Machine Learning and Data Classification (37 papers), Advanced Multi-Objective Optimization Algorithms (18 papers) and Explainable Artificial Intelligence (XAI) (13 papers). Bernd Bischl collaborates with scholars based in Germany, United States and Netherlands. Bernd Bischl's co-authors include Michel Lang, Joaquin Vanschoren, Jan N. van Rijn, Luı́s Torgo, Olaf Mersmann, Jörg Rahnenführer, Heike Trautmann, Xudong Sun, Andrea Bommert and Claus Weihs and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Bioinformatics and Scientific Reports.

In The Last Decade

Bernd Bischl

106 papers receiving 3.9k 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.

OpenML

2014 576 citations Joaquin Vanschoren, Jan N. van Rijn et al. profile →
Benchmark for filter methods for feature selection in high-dimensional classification data

2019 458 citations Bernd Bischl, Michel Lang et al. profile →
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges

2023 398 citations Bernd Bischl, Martin Binder et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Bernd Bischl Germany	29	1.9k	504	356	334	241	112	4.0k
José Hernández‐Orallo Spain	26	2.0k 1.1×	373 0.7×	498 1.4×	214 0.6×	402 1.7×	110	3.8k
Concha Bielza Spain	32	1.8k 0.9×	405 0.8×	405 1.1×	789 2.4×	333 1.4×	180	4.4k
Senén Barro Spain	30	1.6k 0.8×	285 0.6×	444 1.2×	197 0.6×	370 1.5×	137	4.4k
Kristian Kersting Germany	36	2.2k 1.2×	262 0.5×	571 1.6×	193 0.6×	377 1.6×	229	4.5k
Kang Hao Cheong Singapore	38	1.1k 0.6×	377 0.7×	317 0.9×	175 0.5×	163 0.7×	190	4.2k
Claude Sammut Australia	20	1.7k 0.9×	288 0.6×	636 1.8×	251 0.8×	420 1.7×	112	4.8k
János Abonyi Hungary	35	1.2k 0.6×	171 0.3×	272 0.8×	175 0.5×	249 1.0×	267	4.4k
Joaquin Vanschoren Netherlands	24	1.7k 0.9×	285 0.6×	341 1.0×	137 0.4×	272 1.1×	77	2.9k
Ameet Talwalkar United States	28	2.0k 1.1×	197 0.4×	837 2.4×	198 0.6×	360 1.5×	52	3.8k
Chiranjib Bhattacharyya India	20	1.6k 0.9×	186 0.4×	973 2.7×	354 1.1×	388 1.6×	90	3.4k

Countries citing papers authored by Bernd Bischl

Since Specialization

Citations

This map shows the geographic impact of Bernd Bischl'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 Bernd Bischl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bernd Bischl more than expected).

Fields of papers citing papers by Bernd Bischl

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bernd Bischl. 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 Bernd Bischl. The network helps show where Bernd Bischl may publish in the future.

Co-authorship network of co-authors of Bernd Bischl

This figure shows the co-authorship network connecting the top 25 collaborators of Bernd Bischl. A scholar is included among the top collaborators of Bernd Bischl 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 Bernd Bischl. Bernd Bischl 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

Casalicchio, Giuseppe, et al.. (2024). Correction: Marginal effects for non-linear prediction functions. Data Mining and Knowledge Discovery. 38(6). 4234–4235. 1 indexed citations

Hoffmann, Verena, et al.. (2024). Distributed non-disclosive validation of predictive models by a modified ROC-GLM. BMC Medical Research Methodology. 24(1). 190–190.

Sonabend, Raphael, et al.. (2024). Deep learning for survival analysis: a review. Artificial Intelligence Review. 57(3). 45 indexed citations

Pfisterer, Florian, Matthias Feurer, Katharina Eggensperger, et al.. (2024). Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. Journal of Artificial Intelligence Research. 79. 639–677. 10 indexed citations

Branke, Juergen, et al.. (2023). Bayesian Optimization. 895–912. 4 indexed citations

Pielok, Tobias, Florian Pfisterer, Stefan Coors, et al.. (2023). Multi-Objective Hyperparameter Optimization in Machine Learning—An Overview. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 3(4). 1–50. 36 indexed citations

Molnar, Christoph, Gunnar König, Bernd Bischl, & Giuseppe Casalicchio. (2023). Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach. Data Mining and Knowledge Discovery. 38(5). 2903–2941. 53 indexed citations

Rügamer, David, Bernd Bischl, U. von Toussaint, et al.. (2022). Data augmentation for disruption prediction via robust surrogate models. Journal of Plasma Physics. 88(5). 6 indexed citations

Binder, Martin, Florian Pfisterer, Marc Becker, et al.. (2022). Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. IEEE Transactions on Evolutionary Computation. 26(6). 1336–1350. 2 indexed citations

10.

Pargent, Florian, Florian Pfisterer, Janek Thomas, & Bernd Bischl. (2022). Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. Computational Statistics. 37(5). 2671–2692. 63 indexed citations

11.

Schratz, Patrick, Jannes Muenchow, Eugenia Iturritxa, et al.. (2021). Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques?. Remote Sensing. 13(23). 4832–4832. 18 indexed citations

12.

Pfisterer, Florian, et al.. (2021). mcboost: Multi-Calibration Boosting for R. The Journal of Open Source Software. 6(64). 3453–3453. 1 indexed citations

13.

Albert, Christopher G., et al.. (2021). Orbit Classification and Sensitivity Analysis in Dynamical Systems Using Surrogate Models. MDPI (MDPI AG). 5–5.

14.

Sonabend, Raphael, Franz J. Király, Andreas Bender, Bernd Bischl, & Michel Lang. (2020). mlr3proba: Machine Learning Survival Analysis in R.. arXiv (Cornell University). 1 indexed citations

15.

Stachl, Clemens, Quay Au, Ramona Schoedel, et al.. (2020). Predicting personality from patterns of behavior collected with smartphones. Proceedings of the National Academy of Sciences. 117(30). 17680–17687. 145 indexed citations

16.

Probst, Philipp, Anne‐Laure Boulesteix, & Bernd Bischl. (2019). Tunability: Importance of Hyperparameters of Machine Learning Algorithms. arXiv (Cornell University). 20(53). 1–32. 76 indexed citations

17.

Rijn, Jan N. van, Florian Pfisterer, Janek Thomas, et al.. (2018). Meta learning for defaults: symbolic defaults. Data Archiving and Networked Services (DANS). 2 indexed citations

18.

Bischl, Bernd, Quay Au, Clemens Stachl, et al.. (2017). The PhoneStudy Project. OSF Preprints (OSF Preprints). 2 indexed citations

19.

Lang, Michel, et al.. (2017). batchtools: Tools for R to work on batch systems. The Journal of Open Source Software. 2(10). 135–135. 39 indexed citations

20.

Bischl, Bernd, Uwe Ligges, & Claus Weihs. (2009). Frequency estimation by DFT interpolation: a comparison of methods. Technical reports. 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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