Gal Yona

According to our database, Gal Yona authored at least 19 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers.

Gal Yona, Roee Aharoni, Mor Geva

CoRR, 2024

2023

Surfacing Biases in Large Language Models using Contrastive Input Decoding.

Gal Yona, Or Honovich, Itay Laish, Roee Aharoni

CoRR, 2023

Decision-Making Under Miscalibration.

Guy N. Rothblum, Gal Yona

Proceedings of the 14th Innovations in Theoretical Computer Science Conference, 2023

Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds.

Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Malign Overfitting: Interpolation Can Provably Preclude Invariance.

Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon

CoRR, 2022

Useful Confidence Measures: Beyond the Max Score.

Gal Yona, Amir Feder, Itay Laish

CoRR, 2022

Active learning with label comparisons.

Gal Yona, Shay Moran, Gal Elidan, Amir Globerson

Proceedings of the Uncertainty in Artificial Intelligence, 2022

On Fairness and Stability in Two-Sided Matchings.

Gili Karni, Guy N. Rothblum, Gal Yona

Proceedings of the 13th Innovations in Theoretical Computer Science Conference, 2022

Beyond Bernoulli: Generating Random Outcomes that cannot be Distinguished from Nature.

Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, Gal Yona

Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

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

Revisiting Sanity Checks for Saliency Maps.

Gal Yona, Daniel Greenfeld

CoRR, 2021

Consider the Alternatives: Navigating Fairness-Accuracy Tradeoffs via Disqualification.

Guy N. Rothblum, Gal Yona

CoRR, 2021

Multi-group Agnostic PAC Learnability.

Guy N. Rothblum, Gal Yona

Proceedings of the 38th International Conference on Machine Learning, 2021

Who's Responsible? Jointly Quantifying the Contribution of the Learning Algorithm and Data.

Gal Yona, Amirata Ghorbani, James Zou

Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

2020

Outcome Indistinguishability.

Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, Gal Yona

Electron. Colloquium Comput. Complex., 2020

Preference-Informed Fairness.

Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum, Gal Yona

Proceedings of the 11th Innovations in Theoretical Computer Science Conference, 2020

2019

Who's responsible? Jointly quantifying the contribution of the learning algorithm and training data.

Gal Yona, Amirata Ghorbani, James Y. Zou

CoRR, 2019

Learning from Outcomes: Evidence-Based Rankings.

Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, Gal Yona

Proceedings of the 60th IEEE Annual Symposium on Foundations of Computer Science, 2019

2018

Probably Approximately Metric-Fair Learning.

Gal Yona, Guy N. Rothblum

Proceedings of the 35th International Conference on Machine Learning, 2018