  In this paper, we consider a new low-quality label learning problem: learning
time series detection models from temporally imprecise labels. In this problem,
the data consist of a set of input time series, and supervision is provided by
a sequence of noisy time stamps corresponding to the occurrence of positive
class events. Such temporally imprecise labels commonly occur in areas like
mobile health research where human annotators are tasked with labeling the
occurrence of very short duration events. We propose a general learning
framework for this problem that can accommodate different base classifiers and
noise models. We present results on real mobile health data showing that the
proposed framework significantly outperforms a number of alternatives including
assuming that the label time stamps are noise-free, transforming the problem
into the multiple instance learning framework, and learning on labels that were
manually re-aligned.
