Johannes Lederer

1.1k total citations
30 papers, 311 citations indexed

About

Johannes Lederer is a scholar working on Statistics and Probability, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Johannes Lederer has authored 30 papers receiving a total of 311 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Statistics and Probability, 11 papers in Artificial Intelligence and 8 papers in Computational Mechanics. Recurrent topics in Johannes Lederer's work include Statistical Methods and Inference (15 papers), Sparse and Compressive Sensing Techniques (8 papers) and Bayesian Methods and Mixture Models (4 papers). Johannes Lederer is often cited by papers focused on Statistical Methods and Inference (15 papers), Sparse and Compressive Sensing Techniques (8 papers) and Bayesian Methods and Mixture Models (4 papers). Johannes Lederer collaborates with scholars based in Germany, United States and Switzerland. Johannes Lederer's co-authors include Mohamed Hebiri, Arnak S. Dalalyan, Sara van de Geer, Yiyuan She, Florentina Bunea, Christian L. Müller, David Gold, Wei Li, Martin J. Wainwright and Sergio Guadarrama and has published in prestigious journals such as Nucleic Acids Research, IEEE Transactions on Information Theory and Journal of Econometrics.

In The Last Decade

Johannes Lederer

27 papers receiving 302 citations

Peers

Johannes Lederer
Comparison fields: 5 of 78
  • Statistics and Probability 155
  • Computational Mechanics 80
  • Artificial Intelligence 73
  • Molecular Biology 35
  • Control and Systems Engineering 30
Replace Mohamed Hebiri with:
Mohamed Hebiri France
Daniel Gervini United States
Artin Armagan United States
Qing Mai United States
Arne Kovac United Kingdom
T. Krishnan India
Lu Lin China
Wentao Huang Taiwan
Aimé Lachal France
Ching‐Kang Ing Taiwan
Mohamed Hebiri France View profile →
Citations per field, relative to Johannes Lederer
Johannes Lederer · 1×
Citations per year, relative to Johannes Lederer
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Countries citing papers authored by Johannes Lederer

Since Specialization
Citations

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

Fields of papers citing papers by Johannes Lederer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Johannes Lederer

This figure shows the co-authorship network connecting the top 25 collaborators of Johannes Lederer. A scholar is included among the top collaborators of Johannes Lederer 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 Johannes Lederer. Johannes Lederer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 0
2 2
3 0
4 3
5 1
6 5
7 0
8 2
9 3
10 4
11 13
12 3
13
A practical scheme and fast algorithm to tune the lasso with optimality guarantees
12
14 2
15 14
16 3
17 5
18 49
19 28
20 57

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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