2024
NIPS
NeurIPS 2024
Improving Subgroup Robustness via Data Selection
Abstract
Machine learning models can often fail on subgroups that are underrepresentedduring training. While dataset balancing can improve performance onunderperforming groups, it requires access to training group annotations and canend up removing large portions of the dataset. In this paper, we introduceData Debiasing with Datamodels (D3M), a debiasing approachwhich isolates and removes specific training examples that drive the model'sfailures on minority groups. Our approach enables us to efficiently traindebiased classifiers while removing only a small number of examples, and doesnot require training group annotations or additional hyperparameter tuning.
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Keyword Pioneer
— data debiasing
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Interdisciplinary Bridge
— Deep Learning and Machine Learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Learning Types > Weakly Supervised Learning
Machine Learning > Application Areas > Fairness
Machine Learning > Learning Types > Representation Learning
Machine Learning > Core Methods > Optimization
Machine Learning > Learning Types > Fairness
Deep Learning > Learning Types > Representation Learning
Machine Learning > Learning Types > Robustness