Learning more about active learning

In many learning tasks, there are abundant unlabeled samples but the number of labeled training samples is limited, because labeling the samples requires the efforts of human annotators and expertise. There are three major techniques for labeling the ...

We investigate a topic at the interface of machine learning and cognitive science. Human active learning, where learners can actively query the world for information, is contrasted with passive learning from random examples. Furthermore, we compare human ...

In standard passive imitation learning, the goal is to learn a policy that performs as well as a target policy by passively observing full execution trajectories of it. Unfortunately, generating such trajectories can require substantial expert effort and ...