Probably Approximately Metric-Fair Learning. - Gal Yona; Guy N. Rothblum (ICML, 2018)

Learning from Outcomes: Evidence-Based Rankings. - Cynthia Dwork; Michael P. Kim; Omer Reingold; Guy N. Rothblum; Gal Yona (FOCS, 2019)

Who's responsible? Jointly quantifying the contribution of the learning algorithm and training data. - Gal Yona; Amirata Ghorbani; James Y. Zou (CoRR, 2019)

Preference-Informed Fairness. - Michael P. Kim; Aleksandra Korolova; Guy N. Rothblum; Gal Yona (ITCS, 2020)

Outcome Indistinguishability. - Cynthia Dwork; Michael P. Kim; Omer Reingold; Guy N. Rothblum; Gal Yona (Electron. Colloquium Comput. Complex., 2020)

Who's Responsible? Jointly Quantifying the Contribution of the Learning Algorithm and Data. - Gal Yona; Amirata Ghorbani; James Zou 001 (AIES, 2021)

Multi-group Agnostic PAC Learnability. - Guy N. Rothblum; Gal Yona (ICML, 2021)

Consider the Alternatives: Navigating Fairness-Accuracy Tradeoffs via Disqualification. - Guy N. Rothblum; Gal Yona (CoRR, 2021)

Revisiting Sanity Checks for Saliency Maps. - Gal Yona; Daniel Greenfeld (CoRR, 2021)

Addressing bias in prediction models by improving subpopulation calibration. - Noam Barda; Gal Yona; Guy N. Rothblum; Philip Greenland; Morton Leibowitz; Ran D. Balicer; Eitan Bachmat; Noa Dagan (J. Am. Medical Informatics Assoc., 2021)

Beyond Bernoulli: Generating Random Outcomes that cannot be Distinguished from Nature. - Cynthia Dwork; Michael P. Kim; Omer Reingold; Guy N. Rothblum; Gal Yona (ALT, 2022)

On Fairness and Stability in Two-Sided Matchings. - Gili Karni; Guy N. Rothblum; Gal Yona (ITCS, 2022)

Active learning with label comparisons. - Gal Yona; Shay Moran; Gal Elidan; Amir Globerson (UAI, 2022)

Useful Confidence Measures: Beyond the Max Score. - Gal Yona; Amir Feder; Itay Laish (CoRR, 2022)

Malign Overfitting: Interpolation Can Provably Preclude Invariance. - Yoav Wald; Gal Yona; Uri Shalit; Yair Carmon (CoRR, 2022)

Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds. - Yoav Wald; Gal Yona; Uri Shalit; Yair Carmon (ICLR, 2023)

Decision-Making Under Miscalibration. - Guy N. Rothblum; Gal Yona (ITCS, 2023)

Surfacing Biases in Large Language Models using Contrastive Input Decoding. - Gal Yona; Or Honovich; Itay Laish; Roee Aharoni (CoRR, 2023)

Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers. - Gal Yona; Roee Aharoni; Mor Geva (ACL (1), 2024)

Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations? - Zorik Gekhman; Gal Yona; Roee Aharoni; Matan Eyal; Amir Feder; Roi Reichart; Jonathan Herzig (EMNLP, 2024)

Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words? - Gal Yona; Roee Aharoni; Mor Geva (EMNLP, 2024)

Confidence Improves Self-Consistency in LLMs. - Amir Taubenfeld; Tom Sheffer; Eran Ofek; Amir Feder; Ariel Goldstein; Zorik Gekhman; Gal Yona (ACL (Findings), 2025)

Pathways on the Image Manifold: Image Editing via Video Generation. - Noam Rotstein; Gal Yona; Daniel Silver; Roy Velich; David Bensaïd; Ron Kimmel (CVPR, 2025)

Keep Guessing? When Considering Inference Scaling, Mind the Baselines. - Gal Yona; Or Honovich; Omer Levy; Roee Aharoni (NAACL (Findings), 2025)

Neural Descriptors: Self-supervised Learning of Robust Local Surface Descriptors Using Polynomial Patches. - Gal Yona; Roy Velich; Ehud Rivlin; Ron Kimmel (SSVM (2), 2025)

MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs. - Gabrielle Kaili-May Liu; Gal Yona; Avi Caciularu; Idan Szpektor; Tim G. J. Rudner; Arman Cohan (CoRR, 2025)