Bernhard Sch lkopf
- Artificial Intelligence top 10%
- Statistical and Nonlinear Physics top 10%
- Computer Vision and Pattern Recognition
- Signal Processing
- Computer Networks and Communications
- Co-authors
- Manuel Gomez-RodriguezDominik JanzingEleni SgouritsaJonas PetersJoris M. MooijKun ZhangVojtěch FrancAlexander Zien
- Topics
- Neural Networks and Applications (2 papers)Bayesian Modeling and Causal Inference (2 papers)Machine Learning and Algorithms (2 papers)
- Journals
- ArXiv.orgMax Planck Institute for Plasma PhysicsarXiv (Cornell University)
- Partner nations
- GermanyNetherlandsCzechia
In The Last Decade
Bernhard Sch lkopf
4 papers receiving 225 citations
Peers
Comparison fields: 5 of 67
- Artificial Intelligence 147
- Statistical and Nonlinear Physics 68
- Computer Vision and Pattern Recognition 40
- Signal Processing 23
- Computer Networks and Communications 21
Countries citing papers authored by Bernhard Sch lkopf
This map shows the geographic impact of Bernhard Sch lkopf'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 Bernhard Sch lkopf with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bernhard Sch lkopf more than expected).
Fields of papers citing papers by Bernhard Sch lkopf
This network shows the impact of papers produced by Bernhard Sch lkopf. 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 Bernhard Sch lkopf. The network helps show where Bernhard Sch lkopf may publish in the future.
Co-authorship network of co-authors of Bernhard Sch lkopf
This figure shows the co-authorship network connecting the top 25 collaborators of Bernhard Sch lkopf. A scholar is included among the top collaborators of Bernhard Sch lkopf 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 Bernhard Sch lkopf. Bernhard Sch lkopf is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | On causal and anticausal learning | 104 |
| 2 | 77 | |
| 3 | Support Vector Machines as Probabilistic Models | 31 |
| 4 | 22 |
About Bernhard Sch lkopf
Bernhard Sch lkopf is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition, having authored 4 papers that have together received 234 indexed citations. Recurring topics across this work include Neural Networks and Applications (2 papers), Bayesian Modeling and Causal Inference (2 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (68 citations), Artificial Intelligence (147 citations) and Statistics and Probability (18 citations). Bernhard Sch lkopf has collaborated with scholars based in Germany, Netherlands and Czechia. Frequent co-authors include Manuel Gomez-Rodriguez, Dominik Janzing, Eleni Sgouritsa, Jonas Peters, Joris M. Mooij, Kun Zhang, Vojtěch Franc, Alexander Zien and Patrik O. Hoyer. Their work appears in journals such as ArXiv.org, Max Planck Institute for Plasma Physics and arXiv (Cornell University).
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.