We investigate an efficient teaching algorithm that adaptively picks these contrastive examples.

A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons

The sample complexities of our algorithms can be quantified in terms of well-known quantities like the extended teaching dimension and haystack dimension.

We work towards General Continual Learning (GCL), where task boundaries blur and the domain and class distributions shift either gradually or suddenly.

Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their