| Developing a COVID-19 mortality risk prediction model when individual-level data are not available N Barda, D Riesel, A Akriv, J Levy, U Finkel, G Yona, D Greenfeld, ... Nature Communications 11 (1), 1-9, 2020 | 142 | 2020 |
| Probably Approximately Metric-Fair Learning G Yona, G Rothblum International Conference on Machine Learning, 5666-5674, 2018 | 100* | 2018 |
| Outcome indistinguishability C Dwork, MP Kim, O Reingold, GN Rothblum, G Yona Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing …, 2021 | 56 | 2021 |
| Addressing bias in prediction models by improving subpopulation calibration N Barda, G Yona, GN Rothblum, P Greenland, M Leibowitz, R Balicer, ... Journal of the American Medical Informatics Association 28 (3), 549-558, 2021 | 51 | 2021 |
| Preference-Informed Fairness MP Kim, A Korolova, GN Rothblum, G Yona arXiv preprint arXiv:1904.01793, 2019 | 40 | 2019 |
| Multi-group agnostic pac learnability GN Rothblum, G Yona International Conference on Machine Learning, 9107-9115, 2021 | 30 | 2021 |
| Learning from Outcomes: Evidence-Based Rankings C Dwork, MP Kim, O Reingold, GN Rothblum, G Yona 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS) 14, 18, 2019 | 28 | 2019 |
| Revisiting Sanity Checks for Saliency Maps G Yona, D Greenfeld arXiv preprint arXiv:2110.14297, 2021 | 19 | 2021 |
| Who's Responsible? Jointly Quantifying the Contribution of the Learning Algorithm and Data G Yona, A Ghorbani, J Zou Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 1034 …, 2021 | 17* | 2021 |
| Beyond Bernoulli: Generating Random Outcomes that cannot be Distinguished from Nature C Dwork, MP Kim, O Reingold, GN Rothblum, G Yona International Conference on Algorithmic Learning Theory, 342-380, 2022 | 10 | 2022 |
| Malign Overfitting: Interpolation Can Provably Preclude Invariance Y Wald, G Yona, U Shalit, Y Carmon arXiv preprint arXiv:2211.15724, 2022 | 9* | 2022 |
| On Fairness and Stability in Two-Sided Matchings G Karni, GN Rothblum, G Yona arXiv preprint arXiv:2111.10885, 2021 | 7 | 2021 |
| A gentle introduction to the discussion on algorithmic fairness G Yona Towards Data Science 5, 2017 | 6 | 2017 |
| Surfacing Biases in Large Language Models using Contrastive Input Decoding G Yona, O Honovich, I Laish, R Aharoni arXiv preprint arXiv:2305.07378, 2023 | 3 | 2023 |
| Decision-Making under Miscalibration GN Rothblum, G Yona arXiv preprint arXiv:2203.09852, 2022 | 3 | 2022 |
| Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers G Yona, R Aharoni, M Geva arXiv preprint arXiv:2401.04695, 2024 | 2 | 2024 |
| Useful Confidence Measures: Beyond the Max Score G Yona, A Feder, I Laish arXiv preprint arXiv:2210.14070, 2022 | 2 | 2022 |
| Active learning with label comparisons G Yona, S Moran, G Elidan, A Globerson Uncertainty in Artificial Intelligence, 2289-2298, 2022 | 2 | 2022 |
| Consider the Alternatives: Navigating Fairness-Accuracy Tradeoffs via Disqualification GN Rothblum, G Yona arXiv preprint arXiv:2110.00813, 2021 | | 2021 |
