  We address challenges of active learning under scarce informational resources
in non-stationary environments. In real-world settings, data labeled and
integrated into a predictive model may become invalid over time. However, the
data can become informative again with switches in context and such changes may
indicate unmodeled cyclic or other temporal dynamics. We explore principles for
discarding, caching, and recalling labeled data points in active learning based
on computations of value of information. We review key concepts and study the
value of the methods via investigations of predictive performance and costs of
acquiring data for simulated and real-world data sets.
