Beyza Ermiş
Impact in
- Computational Mathematics top 1%
- Tensor decomposition and applications
-
- Complex Network Analysis Techniques
Papers in
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- Topic Modeling 3
- Privacy-Preserving Technologies in Data 2
- Advanced Graph Neural Networks 2
- Domain Adaptation and Few-Shot Learning 2
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- Tensor decomposition and applications 5
- Co-authors
- Ali Taylan Cemgil (6 shared papers)Evrim Acar (3 shared papers)Umut Şimşekli (2 shared papers)Giovanni Zappella (4 shared papers)Sara Hooker (4 shared papers)Patrick A. Lewis (2 shared papers)Cédric Archambeau (2 shared papers)Guillaume Bouchard (1 shared paper)
- Journals
- ACM Transactions on Knowledge Discovery from Data (1 paper)Data Mining and Knowledge Discovery (1 paper)Statistics and Computing (1 paper)Uncertainty in Artificial Intelligence (1 paper)CLEF (Working Notes) (1 paper)
In The Last Decade
Beyza Ermiş
14 papers receiving 164 citations
Peers
Comparison fields: 5 of 38
- Computational Mathematics 79
- Statistical and Nonlinear Physics 40
- Artificial Intelligence 88
- Health Informatics 3
- Signal Processing 13
Countries citing papers authored by Beyza Ermiş
This map shows the geographic impact of Beyza Ermiş'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 Beyza Ermiş with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Beyza Ermiş more than expected).
Fields of papers citing papers by Beyza Ermiş
This network shows the impact of papers produced by Beyza Ermiş. 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 Beyza Ermiş. The network helps show where Beyza Ermiş may publish in the future.
Co-authors
The 24 scholars most cited alongside Beyza Ermiş, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 97 | |
| 2 | 2013 | 12 | |
| 3 | 2023 | 10 | |
| 4 | 2020 | 10 | |
| 5 | 2015 | 8 | |
| 6 | Linear bandits with Stochastic Delayed Feedback | 2020 | 8 |
| 7 | 2022 | 6 | |
| 8 | Liver CT Annotation via Generalized Coupled Tensor Factorization. | 2014 | 5 |
| 9 | 2019 | 5 | |
| 10 | 2013 | 4 | |
| 11 | Iterative splits of quadratic bounds for scalable binary tensor factorization | 2014 | 3 |
| 12 | 2024 | 2 | |
| 13 | 2024 | 1 | |
| 14 | Towards Robust Episodic Meta-Learning | 2021 | 1 |
| 15 | 2025 | 0 | |
| 16 | Contextual Bandits under Delayed Feedback. | 2018 | 0 |
| 17 | 2023 | 0 | |
| 18 | 2024 | 0 |
About Beyza Ermiş
Beyza Ermiş is a scholar working on Artificial Intelligence, Computational Mathematics, Computer Vision and Pattern Recognition, Computational Theory and Mathematics and Transportation, having authored 18 papers that have together received 172 indexed citations. Recurring topics across this work include Tensor decomposition and applications (5 papers), Multimodal Machine Learning Applications (3 papers), Topic Modeling (3 papers), Advanced Bandit Algorithms Research (2 papers), Human Mobility and Location-Based Analysis (2 papers), Privacy-Preserving Technologies in Data (2 papers), Advanced Graph Neural Networks (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). The work is most often cited by research in Computational Mathematics (79 citations), Statistical and Nonlinear Physics (40 citations), Artificial Intelligence (88 citations), Health Informatics (3 citations) and Signal Processing (13 citations). Beyza Ermiş has collaborated with scholars based in Türkiye, Germany and Denmark. Frequent co-authors include Ali Taylan Cemgil, Evrim Acar, Umut Şimşekli, Giovanni Zappella, Sara Hooker, Patrick A. Lewis, Cédric Archambeau, Guillaume Bouchard, Burak Acar and Aditya Rawal. Their work appears in journals such as ACM Transactions on Knowledge Discovery from Data, Data Mining and Knowledge Discovery, Statistics and Computing, Uncertainty in Artificial Intelligence and CLEF (Working Notes).
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.