Rank-Variance Scores for Importance Sampling and Data Pruning under Label Contamination
The authors propose Disagreement-Regularized Importance Sampling (DR-IS), a sub-sampling method that uses loss rank disagreement across multiple independent proxies to prevent standard importance sampling from failing under label corruption. This method redirects sampling to high-disagreement boundary examples, offering superior contamination rejection compared to magnitude-based rules.