About

I am a Research Scientist at Google Research Tel Aviv, where I work on making large language models more robust, factual and trustworthy.

Before Google, I completed a PhD in Computer Science at the Weizmann Institute of Science, under the supervision of Guy Rothblum, where I focused on formalizing fairness & non-discrimination in machine learning algorithms.

Contact

You can contact me at: gal.yona at gmail.com

Publications

For an up to date list, check out my Google Scholar.

Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers‏
Gal Yona, Roee Aharoni and Mor Geva
preprint
[paper]

Surfacing Biases in Large Language Models using Contrastive Input Decoding‏
Gal Yona, Or Honovich, Itay Laish and Roee Aharoni‏
preprint
[paper]

Malign Overfitting: Interpolation Can Provably Preclude Invariance
Yoav Wald, Gal Yona, Uri Shalit and Yair Carmon
International Conference on Learning Representations (ICLR) 2023
[paper]

Decision-Making under Miscalibration
Guy N. Rothblum and Gal Yona
Innovations in Theoretical Computer Science (ITCS) 2023
[paper] [video] [code]

Useful Confidence Measures: Beyond the Max Score
Gal Yona, Amir Feder and Itay Laish
Workshop on Distribution Shifts at Neurips 2022
[paper]

Active Learning with Label Comparisons
Gal Yona, Shay Moran, Gal Elidan and Amir Globerson
Uncertainty in Artificial Intelligence (UAI) 2022
[paper] [poster]

Beyond Bernoulli: Generating Random Outcomes that cannot be Distinguished from Nature
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum and Gal Yona
Algorithmic Learning Theory (ALT) 2022
[paper]

On Fairness and Stability in Two-Sided Matchings
Gili Karni, Guy N. Rothblum and Gal Yona
Innovations in Theoretical Computer Science (ITCS) 2022
[paper]

Revisiting Sanity Checks for Saliency Maps
Gal Yona and Daniel Greenfeld
Workshop on eXplainable AI approaches for debugging and diagnosis at Neurips 2021
[paper] [poster]

Consider the Alternatives: Navigating Fairness-Accuracy Tradeoffs via Disqualification
Guy N. Rothblum and Gal Yona
ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) 2021 (Poster)
[paper] [poster]

Multi-group Agnostic PAC Learning
Guy N. Rothblum and Gal Yona
International Conference on Machine Learning (ICML) 2021
[paper] [video]

Who’s responsible? Jointly quantifying the contribution of the learning algorithm and training data
Gal Yona, Amirata Ghorbani and James Zou
Artificial Intelligence, Ethics and Society (AIES) 2021
[paper] [poster]

Outcome Indistinguishability
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum and Gal Yona.
Symposium on Theory of Computing (STOC) 2021
[paper] [video]

Addressing bias in prediction models by improving subpopulation calibration
Noam Barda, Gal Yona, Guy N Rothblum, Philip Greenland, Morton Leibowitz, Ran Balicer, Eitan Bachmat and Noa Dagan Journal of the American Medical Informatics Association (JAMIA) 2020
[paper]

Developing a COVID-19 mortality risk prediction model when individual-level data are not available
Noam Barda, Dan Riesel, Amichay Akriv, Joseph Levy, Uriah Finkel, Gal Yona, Daniel Greenfeld, Shimon Sheiba, Jonathan Somer, Eitan Bachmat, Guy N Rothblum, Uri Shalit, Doron Netzer, Ran Balicer and Noa Dagan
Nature Communications 2020
[paper]

Preference-Informed Fairness
Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum and Gal Yona
Innovations in Theoretical Computer Science (ITCS) 2020
[paper] [talk]

Evidence-Based Rankings
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum and Gal Yona
Foundations of Computer Science (FOCS) 2019
[paper]

Probably Approximately Metric Fair Learning
Guy N. Rothblum and Gal Yona
International Conference on Machine Learning (ICML) 2018
[paper] [talk]