Instance-Dependent PU Learning by Bayesian Optimal Relabeling
arXiv: Learning, Volume abs/1808.02180, 2018.
When learning from positive and unlabelled data, it is a strong assumption that the positive observations are randomly sampled from the distribution of $X$ conditional on $Y = 1$, where X stands for the feature and Y the label. Most existing algorithms are optimally designed under the assumption. However, for many real-world applications,...More
PPT (Upload PPT)