COVER-EL: Class cOVERage-Aware Noise Correction for Crowdsourced Labels via Elimination-Based Inference.
This work proposes a class coverage-aware noise correction method for crowdsourced labels via elimination-based inference (COVER-EL), and provides a probably approximately correct (PAC)-style bound that gives a high-probability guarantee on the overall error after correction.