I’m not yet sure what this is, but I’ve seen these words invoked in machine learning problems with a partially-observed model, whee you hope to simultaneously learn the parameters of the generation process and the observation process: So if I have a bunch of crowd-sourced labels for my data and I wish to use them to train a classifier, but I suspect that my crowd is a little unreliable, then I try to do “weakly supervised” learning when I learn both the true labels and the crowd whimsy process, as a kind of hierarchical model of informative sampling (e.g. MZMG15) Or I might assume no explicit model for the crowd whimsy, but simply that similar data should not be too differently labelled, a.k.a. Label Propagation, which uses graph clustering to infer data labels
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