Active Cost-aware Labeling of Streaming Data. (arXiv:2304.06808v1 [cs.LG])

We study actively labeling streaming data, where an active learner is faced
with a stream of data points and must carefully choose which of these points to
label via an expensive experiment. Such problems frequently arise in
applications such as healthcare and astronomy. We first study a setting when
the data's inputs belong to one of $K$ discrete distributions and formalize
this problem via a loss that captures the labeling cost and the prediction
error. When the labeling cost is …

algorithm applications arxiv astronomy cost data error experiment healthcare labeling loss prediction streaming streaming data study uncertainty