From: NeurIPS.cc/2022/Workshop/DistShift/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: This paper proposes an algorithm for mitigating spurious correlations without training-time annotations of the spurious attributes. The algorithm first trains a standard model, then computes the gradient norm of each training example under the standard model, and finally trains a robust model by upweighting examples based on the gradient norm. As suggested by reviewers, the paper proposes a simple, practical method, and the method has strong empirical performance, outperforming relevant baselines on several datasets.