Privacy in Metalearning and Multitask Learning: Modeling and Separations

Model personalization allows a set of individuals, each facing a different learning task, to train models that are more accurate for each person than those they could develop individually. For example, consider a set of people, each of whom holds a relatively small dataset of photographs labeled with the names of their loved ones that appear in each picture. Each person would like to build a classifier that labels future pictures with the names of people in the picture, but training such an image classifier would take more data than any individual person has. Even though the tasks they want to carry out are different--their photos have different subjects--those tasks share a lot of common structure. By pooling their data, a large group of people could learn the shared components of a good set of classifiers. Each individual could then train the subject-specific components on their own, requiring only a few examples for each subject. Other applications of personalization include next-word prediction on a mobile keyboard, speech recognition, and recommendation systems. The goals of personalization are captured in a variety of formal frameworks, such as multitask learning and metalearning.