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RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks
We study the problem of robust data augmentation for regression tasks in the presence of noisy data.
Quilt: Robust Data Segment Selection against Concept Drifts
However, we contend that explicitly utilizing the drifted data together leads to much better model accuracy and propose Quilt, a data-centric framework for identifying and selecting data segments that maximize model accuracy.
RegMix: Data Mixing Augmentation for Regression
Data augmentation is becoming essential for improving regression performance in critical applications including manufacturing, climate prediction, and finance.
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