92 citations · 308 across the 20 of their papers we have counts for
4 papers · 1 filter
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 $…
In the Eye of the Beholder: Robust Prediction with Causal User Modeling
Amir Feder, Guy Horowitz, Yoav Wald +2
Accurately predicting the relevance of items to users is crucial to the success of many social platforms. Conventional approaches train models on logged historical data; but recomm…
On Calibration and Out-of-domain Generalization
Yoav Wald, Amir Feder, Daniel Greenfeld +1
Out-of-domain (OOD) generalization is a significant challenge for machine learning models. Many techniques have been proposed to overcome this challenge, often focused on learning…
Explaining Classifiers with Causal Concept Effect (CaCE)
Yash Goyal, Amir Feder, Uri Shalit +1
How can we understand classification decisions made by deep neural networks? Many existing explainability methods rely solely on correlations and fail to account for confounding, w…