Active Learning

In many applications, it’s possible to take measurements repeatedly to use for inference. Examples include genetic experiments, environmental sensing, and crowdsourced image tasks. The problem of how to design sequential measurements for machine learning inference is called active learning. We can and should exploit expert knowledge about the signal of interest. However, if we trust a model too much, we may miss a true signal. We have studied active learning algorithms for image clustering/classification and spatial environmental sampling.