2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2024
The Group Robustness is in the Details: Revisiting Finetuning under Spurious Correlations
Tyler LaBonte, John C. Hill, Xinchen Zhang +2
Modern machine learning models are prone to over-reliance on spurious correlations, which can often lead to poor performance on minority groups. In this paper, we identify surprisi…
cs.LG2023★ 2 cited
Towards Last-layer Retraining for Group Robustness with Fewer Annotations
Tyler LaBonte, Vidya Muthukumar, Abhishek Kumar
Empirical risk minimization (ERM) of neural networks is prone to over-reliance on spurious correlations and poor generalization on minority groups. The recent deep feature reweight…