Papers with bias-correction methods

1 papers
Counterfactual Augmentation for Multimodal Learning Under Presentation Bias (2023.findings-emnlp)

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Challenge: In real-world machine learning systems, labels are often derived from user behaviors that the system wishes to encourage.
Approach: They propose a method for correcting presentation bias using generated counterfactual labels by augmentation of the labels by the user.
Outcome: The proposed method improves performance in an oracle setting compared to uncorrected models and existing bias-correction methods.

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