92 citations · 308 across the 20 of their papers we have counts for
4 papers · 1 filter
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
Amir Feder, Katherine A. Keith, Emaad Manzoor +10
A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same…
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions
Daniel Rosenberg, Itai Gat, Amir Feder +1
Deep learning algorithms have shown promising results in visual question answering (VQA) tasks, but a more careful look reveals that they often do not understand the rich signal th…
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…
Model Compression for Domain Adaptation through Causal Effect Estimation
Guy Rotman, Amir Feder, Roi Reichart
Recent improvements in the predictive quality of natural language processing systems are often dependent on a substantial increase in the number of model parameters. This has led t…