Eleni Sgouritsa
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Statistics and Probability
- Signal Processing
- Management Science and Operations Research
- Co-authors
- Dominik JanzingJonas PetersBernhard Sch lkopfJoris M. MooijKun ZhangBernhard SchölkopfPhilipp HennigBernhard Schoelkopf
- Topics
- Bayesian Modeling and Causal Inference (3 papers)Statistical Methods and Inference (1 paper)Tensor decomposition and applications (1 paper)
- Journals
- arXiv (Cornell University)International Conference on Machine LearningMPG.PuRe (Max Planck Society)
- Partner nations
- GermanyNetherlandsUnited States
In The Last Decade
Eleni Sgouritsa
5 papers receiving 135 citations
Peers
Comparison fields: 5 of 40
- Artificial Intelligence 128
- Computer Vision and Pattern Recognition 29
- Statistics and Probability 18
- Signal Processing 13
- Management Science and Operations Research 11
Countries citing papers authored by Eleni Sgouritsa
This map shows the geographic impact of Eleni Sgouritsa'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 Eleni Sgouritsa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eleni Sgouritsa more than expected).
Fields of papers citing papers by Eleni Sgouritsa
This network shows the impact of papers produced by Eleni Sgouritsa. 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 Eleni Sgouritsa. The network helps show where Eleni Sgouritsa may publish in the future.
Co-authorship network of co-authors of Eleni Sgouritsa
This figure shows the co-authorship network connecting the top 25 collaborators of Eleni Sgouritsa. A scholar is included among the top collaborators of Eleni Sgouritsa 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 Eleni Sgouritsa. Eleni Sgouritsa is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Inference of Cause and Effect with Unsupervised Inverse Regression | 17 |
| 2 | 11 | |
| 3 | 4 | |
| 4 | On causal and anticausal learning | 104 |
| 5 | 7 |
About Eleni Sgouritsa
Eleni Sgouritsa is a scholar working on Computational Mathematics, Artificial Intelligence and Statistics and Probability, having authored 5 papers that have together received 143 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (3 papers), Statistical Methods and Inference (1 paper) and Tensor decomposition and applications (1 paper). The work is most often cited by research in Artificial Intelligence (128 citations), Statistics and Probability (18 citations) and Computational Mathematics (1 citation). Eleni Sgouritsa has collaborated with scholars based in Germany, Netherlands and United States. Frequent co-authors include Dominik Janzing, Jonas Peters, Bernhard Sch lkopf, Joris M. Mooij, Kun Zhang, Bernhard Schölkopf, Philipp Hennig, Bernhard Schoelkopf, Samory Kpotufe and Oliver Stegle. Their work appears in journals such as arXiv (Cornell University), International Conference on Machine Learning and MPG.PuRe (Max Planck Society).
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.