37 citations · 93 across the 25 of their papers we have counts for
11 papers · 1 filter
Malign Overfitting: Interpolation Can Provably Preclude Invariance
Yoav Wald, Gal Yona, Uri Shalit +1
Learned classifiers should often possess certain invariance properties meant to encourage fairness, robustness, or out-of-distribution generalization. However, multiple recent work…
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 $…
Active Learning with Label Comparisons
Gal Yona, Shay Moran, Gal Elidan +1
Supervised learning typically relies on manual annotation of the true labels. When there are many potential classes, searching for the best one can be prohibitive for a human annot…
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…
Who's responsible? Jointly quantifying the contribution of the learning algorithm and training data
Gal Yona, Amirata Ghorbani, James Zou
A learning algorithm trained on a dataset is revealed to have poor performance on some subpopulation at test time. Where should the responsibility for this lay? It can be a…
Preference-Informed Fairness
Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum +1
We study notions of fairness in decision-making systems when individuals have diverse preferences over the possible outcomes of the decisions. Our starting point is the seminal wor…
Probably Approximately Metric-Fair Learning
Guy N. Rothblum, Gal Yona
The seminal work of Dwork {\em et al.} [ITCS 2012] introduced a metric-based notion of individual fairness. Given a task-specific similarity metric, their notion required that ever…