Predictive Inference with Weak Supervision. (arXiv:2201.08315v1 [stat.ML])

The expense of acquiring labels in large-scale statistical machine learning
makes partially and weakly-labeled data attractive, though it is not always
apparent how to leverage such data for model fitting or validation. We present
a methodology to bridge the gap between partial supervision and validation,
developing a conformal prediction framework to provide valid predictive
confidence sets -- sets that cover a true label with a prescribed probability,
independent of the underlying distribution -- using weakly labeled data. To do
so, …

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