2 citations · 2 across the 5 of their papers we have counts for
3 papers · 1 filter
On the Unreasonable Effectiveness of Last-layer Retraining
John C. Hill, Tyler LaBonte, Xinchen Zhang +1
Last-layer retraining (LLR) methods -- wherein the last layer of a neural network is reinitialized and retrained on a held-out set following ERM training -- have garnered interest…
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…
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…