56 citations · 70 across the 4 of their papers we have counts for
4 papers
Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling
Junhyun Nam, Sangwoo Mo, Jaeho Lee +1
To capture the relationship between samples and labels, conditional generative models often inherit spurious correlations from the training dataset. This can result in label-condit…
Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation
Junhyun Nam, Jaehyung Kim, Jaeho Lee +1
The paradigm of worst-group loss minimization has shown its promise in avoiding to learn spurious correlations, but requires costly additional supervision on spurious attributes. T…
Learning from Failure: Training Debiased Classifier from Biased Classifier
Junhyun Nam, Hyuntak Cha, Sungsoo Ahn +2
Neural networks often learn to make predictions that overly rely on spurious correlation existing in the dataset, which causes the model to be biased. While previous work tackles t…
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles
Jung-hun Kim, Se-Young Yun, Minchan Jeong +3
We study contextual linear bandit problems under feature uncertainty, where the features are noisy and have missing entries. To address the challenges posed by this noise, we analy…