20 citations · 38 across the 5 of their papers we have counts for
6 papers · 1 filter
MaskTune: Mitigating Spurious Correlations by Forcing to Explore
Saeid Asgari Taghanaki, Aliasghar Khani, Fereshte Khani +4
A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting…
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…
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…
Feature Noise Induces Loss Discrepancy Across Groups
Fereshte Khani, Percy Liang
The performance of standard learning procedures has been observed to differ widely across groups. Recent studies usually attribute this loss discrepancy to an information deficienc…
Maximum Weighted Loss Discrepancy
Fereshte Khani, Aditi Raghunathan, Percy Liang
Though machine learning algorithms excel at minimizing the average loss over a population, this might lead to large discrepancies between the losses across groups within the popula…
Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings
Fereshte Khani, Martin Rinard, Percy Liang
Can we train a system that, on any new input, either says "don't know" or makes a prediction that is guaranteed to be correct? We answer the question in the affirmative provided ou…