12 citations · 14 across the 6 of their papers we have counts for
8 papers
Useful Confidence Measures: Beyond the Max Score
Gal Yona, Amir Feder, Itay Laish
An important component in deploying machine learning (ML) in safety-critic applications is having a reliable measure of confidence in the ML model's predictions. For a classifier $…
Decision-Making under Miscalibration
Guy N. Rothblum, Gal Yona
ML-based predictions are used to inform consequential decisions about individuals. How should we use predictions (e.g., risk of heart attack) to inform downstream binary classifica…
Revisiting Sanity Checks for Saliency Maps
Gal Yona, Daniel Greenfeld
Saliency methods are a popular approach for model debugging and explainability. However, in the absence of ground-truth data for what the correct maps should be, evaluating and com…
Consider the Alternatives: Navigating Fairness-Accuracy Tradeoffs via Disqualification
Guy N. Rothblum, Gal Yona
In many machine learning settings there is an inherent tension between fairness and accuracy desiderata. How should one proceed in light of such trade-offs? In this work we introdu…
Multi-group Agnostic PAC Learnability
Guy N Rothblum, Gal Yona
An agnostic PAC learning algorithm finds a predictor that is competitive with the best predictor in a benchmark hypothesis class, where competitiveness is measured with respect to…
Outcome Indistinguishability
Cynthia Dwork, Michael P. Kim, Omer Reingold +2
Prediction algorithms assign numbers to individuals that are popularly understood as individual "probabilities" -- what is the probability of 5-year survival after cancer diagnosis…