Gal Yona

439 total citations
11 papers, 172 citations indexed

About

Gal Yona is a scholar working on Artificial Intelligence, Economics and Econometrics and Statistics and Probability. According to data from OpenAlex, Gal Yona has authored 11 papers receiving a total of 172 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 2 papers in Economics and Econometrics and 2 papers in Statistics and Probability. Recurrent topics in Gal Yona's work include Machine Learning in Healthcare (2 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Game Theory and Voting Systems (2 papers). Gal Yona is often cited by papers focused on Machine Learning in Healthcare (2 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Game Theory and Voting Systems (2 papers). Gal Yona collaborates with scholars based in Israel and United States. Gal Yona's co-authors include Guy N. Rothblum, Noam Barda, Noa Dagan, Ran D. Balicer, Eitan Bachmat, Philip Greenland, Morton Leibowitz, Doron Netzer, Dan Riesel and Joseph Levy and has published in prestigious journals such as Nature Communications, Journal of the American Medical Informatics Association and International Conference on Machine Learning.

In The Last Decade

Gal Yona

10 papers receiving 166 citations

Peers

Gal Yona

AI

RNMI

ID

HI

EPIDE

Zachary H. Strasser United States

Luca Mingardi United States

Sharvari Shukla India

Jonathan Somer Israel

Christophe Gaudet-Blavignac Switzerland

Gurjit S. Randhawa Canada

Patrik Bächtiger United Kingdom

Salem Argaw United States

Zhaozhi Qian United Kingdom

Rubina Rizvi United States

Zachary H. Strasser United States

Gal Yona

58 ×0.9

AI

53 ×1.6

RNMI

74 ×2.2

ID

22 ×1.0

HI

29 ×1.4

EPIDE

Citations per year, relative to Gal Yona Gal Yona (= 1×) peers Zachary H. Strasser

Countries citing papers authored by Gal Yona

Since Specialization

Citations

This map shows the geographic impact of Gal Yona'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 Gal Yona with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gal Yona more than expected).

Fields of papers citing papers by Gal Yona

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gal Yona. 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 Gal Yona. The network helps show where Gal Yona may publish in the future.

Co-authorship network of co-authors of Gal Yona

This figure shows the co-authorship network connecting the top 25 collaborators of Gal Yona. A scholar is included among the top collaborators of Gal Yona 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 Gal Yona. Gal Yona is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

1.

Ofek, E. O., et al.. (2025). Confidence Improves Self-Consistency in LLMs. 20090–20111.

2.

Yona, Gal, Roee Aharoni, & Mor Geva. (2024). Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers. 6737–6751. 2 indexed citations

3.

Yona, Gal, Roee Aharoni, Matan Eyal, et al.. (2024). Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?. 7765–7784. 18 indexed citations

4.

Yona, Gal, Roee Aharoni, & Mor Geva. (2024). Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?. 7752–7764. 4 indexed citations

5.

Yona, Gal, Amirata Ghorbani, & James Zou. (2021). Who's Responsible? Jointly Quantifying the Contribution of the Learning Algorithm and Data. 1034–1041. 5 indexed citations

6.

Dwork, Cynthia, Michael P. Kim, Omer Reingold, Guy N. Rothblum, & Gal Yona. (2021). Outcome indistinguishability. 1095–1108. 11 indexed citations

7.

Barda, Noam, Gal Yona, Guy N. Rothblum, et al.. (2020). Addressing bias in prediction models by improving subpopulation calibration. Journal of the American Medical Informatics Association. 28(3). 549–558. 40 indexed citations

8.

Barda, Noam, Dan Riesel, Joseph Levy, et al.. (2020). Developing a COVID-19 mortality risk prediction model when individual-level data are not available. Nature Communications. 11(1). 4439–4439. 77 indexed citations

9.

Kim, Michael P., Aleksandra Korolova, Guy N. Rothblum, & Gal Yona. (2020). Preference-informed fairness. 546–546. 8 indexed citations

10.

Dwork, Cynthia, Michael P. Kim, Omer Reingold, Guy N. Rothblum, & Gal Yona. (2019). Learning from Outcomes: Evidence-Based Rankings. 106–125. 4 indexed citations

11.

Yona, Gal & Guy N. Rothblum. (2018). Probably Approximately Metric-Fair Learning. International Conference on Machine Learning. 5680–5688. 3 indexed citations

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.

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