Machine Teaching with Generative Models for Human Learning


In this work, we tackle a specific task that can aid experimental scientists in the era of big data, given a large dataset of annotated samples divided into different classes, how can we best teach human researchers what is the difference between the classes? To accomplish this, we develop a new framework combining machine teaching and generative models that generates a small set of synthetic teaching examples for each class. This set will aim to contain all the information necessary to distinguish between the classes. To validate our framework, we perform a human study in which human subjects learn how to classify various datasets using a small teaching set generated by our framework as well as several subset selection algorithms.

Third ICML Workshopon Human In the Loop Learning (ICML 2021 HILL)