8 citations · 13 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.ML2017★ 8 cited
Nonparametric Preference Completion
Julian Katz-Samuels, Clayton Scott
We consider the task of collaborative preference completion: given a pool of items, a pool of users and a partially observed item-user rating matrix, the goal is to recover the \em…