Noisy Label Learning

Trusted Loss Correction for Noisy Multi-Label Learning ACML 2022 FINE Samples for Learning with Noisy Labels NeurIPS 2021 When Optimizing f-Divergence is Robust with Label Noise ICLR 2021 Learning Halfspaces with Massart Noise Under Structured Distributions COLT 2020 Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning IJCAI 2020 Robust Deep Ordinal Regression under Label Noise ACML 2020 Positive and Unlabeled Learning via Loss Decomposition and Centroid Estimation IJCAI 2018 beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data NeurIPS 2016 Reputation-based Worker Filtering in Crowdsourcing NeurIPS 2014 Learning with Noisy Labels NeurIPS 2013