Search Results - Gal Yona

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  1. 1
    Developing a COVID-19 mortality risk prediction model when individual-level data are not available

    Developing a COVID-19 mortality risk prediction model when individual-level data are not available by Barda, Noam, Riesel, Dan, Akriv, Amichay, Levy, Joseph, Finkel, Uriah, Yona, Gal, Greenfeld, Daniel, Sheiba, Shimon, Somer, Jonathan, Bachmat, Eitan, Rothblum, Guy N., Shalit, Uri, Netzer, Doron, Balicer, Ran, Dagan, Noa

    Published in Nature communications
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  2. 2
    Addressing bias in prediction models by improving subpopulation calibration

    Addressing bias in prediction models by improving subpopulation calibration by Barda, Noam, Yona, Gal, Rothblum, Guy N, Greenland, Philip, Leibowitz, Morton, Balicer, Ran, Bachmat, Eitan, Dagan, Noa

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  3. 3
    Revisiting Sanity Checks for Saliency Maps

    Revisiting Sanity Checks for Saliency Maps by Gal Yona, Greenfeld, Daniel

    Published in arXiv.org
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  4. 4
    Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?

    Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words? by Gal Yona, Aharoni, Roee, Geva, Mor

    Published in arXiv.org
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  5. 5
    Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers

    Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers by Gal Yona, Aharoni, Roee, Geva, Mor

    Published in arXiv.org
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  6. 6
    Decision-Making under Miscalibration

    Decision-Making under Miscalibration by Rothblum, Guy N, Gal Yona

    Published in arXiv.org
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  7. 7
    Consider the Alternatives: Navigating Fairness-Accuracy Tradeoffs via Disqualification

    Consider the Alternatives: Navigating Fairness-Accuracy Tradeoffs via Disqualification by Rothblum, Guy N, Gal Yona

    Published in arXiv.org
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  8. 8
    Keep Guessing? When Considering Inference Scaling, Mind the Baselines

    Keep Guessing? When Considering Inference Scaling, Mind the Baselines by Gal Yona, Or Honovich, Levy, Omer, Aharoni, Roee

    Published in arXiv.org
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  9. 9
    Multi-group Agnostic PAC Learnability

    Multi-group Agnostic PAC Learnability by Rothblum, Guy N, Gal Yona

    Published in arXiv.org
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  10. 10
    Malign Overfitting: Interpolation Can Provably Preclude Invariance

    Malign Overfitting: Interpolation Can Provably Preclude Invariance by Wald, Yoav, Gal Yona, Shalit, Uri, Carmon, Yair

    Published in arXiv.org
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  11. 11
    Useful Confidence Measures: Beyond the Max Score

    Useful Confidence Measures: Beyond the Max Score by Gal Yona, Feder, Amir, Laish, Itay

    Published in arXiv.org
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  12. 12
    On Fairness and Stability in Two-Sided Matchings

    On Fairness and Stability in Two-Sided Matchings by Karni, Gili, Rothblum, Guy N, Gal Yona

    Published in arXiv.org
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  13. 13
    Surfacing Biases in Large Language Models using Contrastive Input Decoding

    Surfacing Biases in Large Language Models using Contrastive Input Decoding by Gal Yona, Or Honovich, Laish, Itay, Aharoni, Roee

    Published in arXiv.org
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  14. 14
    Active Learning with Label Comparisons

    Active Learning with Label Comparisons by Gal Yona, Moran, Shay, Elidan, Gal, Globerson, Amir

    Published in arXiv.org
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  15. 15
    Pathways on the Image Manifold: Image Editing via Video Generation

    Pathways on the Image Manifold: Image Editing via Video Generation by Rotstein, Noam, Gal Yona, Silver, Daniel, Velich, Roy, Bensaïd, David, Kimmel, Ron

    Published in arXiv.org
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  16. 16
    Who's responsible? Jointly quantifying the contribution of the learning algorithm and training data

    Who's responsible? Jointly quantifying the contribution of the learning algorithm and training data by Gal Yona, Ghorbani, Amirata, Zou, James

    Published in arXiv.org
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  17. 17
    Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?

    Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations? by Gekhman, Zorik, Gal Yona, Aharoni, Roee, Matan Eyal, Feder, Amir, Reichart, Roi, Herzig, Jonathan

    Published in arXiv.org
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  18. 18
    Probably Approximately Metric-Fair Learning

    Probably Approximately Metric-Fair Learning by Rothblum, Guy N, Gal Yona

    Published in arXiv.org
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  19. 19
    Preference-Informed Fairness

    Preference-Informed Fairness by Kim, Michael P, Korolova, Aleksandra, Rothblum, Guy N, Gal Yona

    Published in arXiv.org
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  20. 20
    Outcome Indistinguishability

    Outcome Indistinguishability by Dwork, Cynthia, Kim, Michael P, Reingold, Omer, Rothblum, Guy N, Gal Yona

    Published in arXiv.org
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