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Information Theoretic Meta Learning with Gaussian Processes
Abstract

We formulate meta learning using information theoretic concepts such as mutual information and the information bottleneck. The idea is to learn a stochastic representation or encoding of the task description, given by a training or support set, that is highly informative about predicting the validation set. By making use of variational approximations to the mutual information we derive a general and tractable framework for meta learning. We particularly develop new memory-based meta learning algorithms based on Gaussian processes and derive extensions that combine memory and gradient based meta learning. We demonstrate our method on few-shot regression and classification by using standard benchmarks such as Omniglot, mini-Imagenet and Augmented Omniglot.

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