Christoph Molnar
- Artificial Intelligence top 5%
- Rheumatology top 5%
- Immunology
- Environmental Engineering top 10%
- Global and Planetary Change
- Co-authors
- Bernd BischlGunnar KönigAlexandre M.J.‐C. WadouxGiuseppe CasalicchioDésirée van der HeijdeRobert LandewéPascale ExerLukas Wildi
- Topics
- Explainable Artificial Intelligence (XAI) (6 papers)Machine Learning and Data Classification (5 papers)Bayesian Modeling and Causal Inference (2 papers)
- Partner nations
- GermanyAustriaNetherlands
In The Last Decade
Christoph Molnar
12 papers receiving 989 citations
Hit Papers
Peers
Comparison fields: 5 of 168
- Artificial Intelligence 309
- Rheumatology 183
- Immunology 112
- Environmental Engineering 82
- Global and Planetary Change 79
Countries citing papers authored by Christoph Molnar
This map shows the geographic impact of Christoph Molnar'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 Christoph Molnar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christoph Molnar more than expected).
Fields of papers citing papers by Christoph Molnar
This network shows the impact of papers produced by Christoph Molnar. 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 Christoph Molnar. The network helps show where Christoph Molnar may publish in the future.
Co-authorship network of co-authors of Christoph Molnar
This figure shows the co-authorship network connecting the top 25 collaborators of Christoph Molnar. A scholar is included among the top collaborators of Christoph Molnar 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 Christoph Molnar. Christoph Molnar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 9 | |
| 4 | 53 | |
| 5 | 49 | |
| 6 | 39 | |
| 7 | Interpretable Machine Learningbreakdown → | 238 |
| 8 | Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition. | 9 |
| 9 | iml: An R package for Interpretable Machine Learningbreakdown → | 382 |
| 10 | 186 | |
| 11 | 20 | |
| 12 | 25 |
About Christoph Molnar
Christoph Molnar is a scholar working on Artificial Intelligence, Statistics and Probability and Pharmacy, having authored 12 papers that have together received 1.0k indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (6 papers), Machine Learning and Data Classification (5 papers) and Bayesian Modeling and Causal Inference (2 papers). The work is most often cited by research in Health Informatics (27 citations), Rheumatology (183 citations) and Artificial Intelligence (309 citations). Christoph Molnar has collaborated with scholars based in Germany, Austria and Netherlands. Frequent co-authors include Bernd Bischl, Gunnar König, Alexandre M.J.‐C. Wadoux, Giuseppe Casalicchio, Désirée van der Heijde, Robert Landewé, Pascale Exer, Lukas Wildi, Giorgio Tamborrini and Xenofon Baraliakos. Their work appears in journals such as Annals of the Rheumatic Diseases, Geoderma and Data Mining and Knowledge Discovery.
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.