20 citations · 53 across the 8 of their papers we have counts for
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cs.LG2020★ 3 cited
Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately
Fereshte Khani, Percy Liang
The presence of spurious features interferes with the goal of obtaining robust models that perform well across many groups within the population. A natural remedy is to remove spur…
cs.LG2020★ 9 cited
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness
Sang Michael Xie, Ananya Kumar, Robbie Jones +3
Consider a prediction setting with few in-distribution labeled examples and many unlabeled examples both in- and out-of-distribution (OOD). The goal is to learn a model which perfo…