Metalearning with Very Few Samples Per Task

Abstract

Metalearning and multitask learning are two frameworks for solving a group of related learning tasks more efficiently than we could hope to solve each of the individual tasks on their own. In multitask learning, we are given a fixed set of related learning tasks and need to output one accurate model per task, whereas in metalearning we are given tasks that are drawn i.i.d. from a metadistribution and need to output some common information that can be easily specialized to new tasks from the metadistribution. We consider a binary classification setting where tasks are related by a shared representation, that is, every task P can be solved by a classifier of the form f_P h where h H is a map from features to a representation space that is shared across tasks, and f_P F is a task-specific classifier from the representation space to labels. The main question we ask is how much data do we need to metalearn a good representation? Here, the amount of data is measured in terms of the number of tasks t that we need to see and the number of samples n per task. We focus on the regime where n is extremely small. Our main result shows that, in a distribution-free setting where the feature vectors are in R^d, the representation is a linear map from R^d R^k, and the task-specific classifiers are halfspaces in R^k, we can metalearn a representation with error using n = k+2 samples per task, and d (1/)^O(k) tasks. Learning with so few samples per task is remarkable because metalearning would be impossible with k+1 samples per task, and because we cannot even hope to learn an accurate task-specific classifier with k+2 samples per task. Our work also yields a characterization of distribution-free multitask learning and reductions between meta and multitask learning.

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Reproductions