Fereshte Khani
Impact in
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- Topic Modeling
- Natural Language Processing Techniques
- Speech and dialogue systems
- Domain Adaptation and Few-Shot Learning
- Adversarial Robustness in Machine Learning
- Explainable Artificial Intelligence (XAI)
- Hate Speech and Cyberbullying Detection
Papers in
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- Explainable Artificial Intelligence (XAI) 3
- Adversarial Robustness in Machine Learning 2
- Machine Learning and Data Classification 2
- Advanced Clustering Algorithms Research 1
- Topic Modeling 1
- Multi-Agent Systems and Negotiation 1
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- Statistical Methods and Inference 2
- Co-authors
- Percy Liang (4 shared papers)Reid Pryzant (1 shared paper)Noah D. Goodman (1 shared paper)Zexue He (1 shared paper)Marco Túlio Ribeiro (1 shared paper)Hamid Beigy (1 shared paper)Ahmad Ali Abin (1 shared paper)
- Journals
- Transactions of the Association for Computational Linguistics (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- United StatesIran
In The Last Decade
Fereshte Khani
7 papers receiving 71 citations
Peers
Comparison fields: 5 of 42
- Health Informatics 2
- Artificial Intelligence 44
- Computer Vision and Pattern Recognition 11
- Information Systems 9
- Cultural Studies 3
Countries citing papers authored by Fereshte Khani
This map shows the geographic impact of Fereshte Khani'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 Fereshte Khani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fereshte Khani more than expected).
Fields of papers citing papers by Fereshte Khani
This network shows the impact of papers produced by Fereshte Khani. 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 Fereshte Khani. The network helps show where Fereshte Khani may publish in the future.
Co-authors
The 7 scholars most cited alongside Fereshte Khani, 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 | 2024 | 39 | |
| 2 | 2021 | 16 | |
| 3 | 2018 | 10 | |
| 4 | 2023 | 4 | |
| 5 | 2013 | 4 | |
| 6 | Noise Induces Loss Discrepancy Across Groups for Linear Regression. | 2019 | 2 |
| 7 | 2019 | 1 | |
| 8 | 2022 | 0 |
About Fereshte Khani
Fereshte Khani is a scholar working on Artificial Intelligence, Statistics and Probability, Information Systems, Statistical and Nonlinear Physics and Signal Processing, having authored 8 papers that have together received 76 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (3 papers), Adversarial Robustness in Machine Learning (2 papers), Machine Learning and Data Classification (2 papers), Statistical Methods and Inference (2 papers), Advanced Clustering Algorithms Research (1 paper), Data Mining Algorithms and Applications (1 paper), Topic Modeling (1 paper) and Multi-Agent Systems and Negotiation (1 paper). The work is most often cited by research in Health Informatics (2 citations), Artificial Intelligence (44 citations), Computer Vision and Pattern Recognition (11 citations), Information Systems (9 citations) and Cultural Studies (3 citations). Fereshte Khani has collaborated with scholars based in United States and Iran. Frequent co-authors include Percy Liang, Reid Pryzant, Noah D. Goodman, Zexue He, Marco Túlio Ribeiro, Hamid Beigy and Ahmad Ali Abin. Their work appears in journals such as Transactions of the Association for Computational Linguistics 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.