5 citations · 10 across the 2 of their papers we have counts for
2 papers
stat.ML2020★ 5 cited
Learning from Label Proportions: A Mutual Contamination Framework
Clayton Scott, Jianxin Zhang
Learning from label proportions (LLP) is a weakly supervised setting for classification in which unlabeled training instances are grouped into bags, and each bag is annotated with…
stat.ML2019★ 5 cited
Learning from Multiple Corrupted Sources, with Application to Learning from Label Proportions
Clayton Scott, Jianxin Zhang
We study binary classification in the setting where the learner is presented with multiple corrupted training samples, with possibly different sample sizes and degrees of corruptio…