# Uncertainty in Model-Agnostic Meta-Learning using Variational Inference

## TL;DR

This paper presents a Bayesian meta-learning algorithm using variational inference that learns a distribution over model priors, improving calibration and accuracy in few-shot learning tasks across classification and regression.

## Contribution

It introduces a novel Bayesian meta-learning method applicable to any model architecture, with rigorous formulation and state-of-the-art results in few-shot classification.

## Key findings

- Achieves state-of-the-art calibration on Omniglot and Mini-ImageNet
- Provides competitive results in multi-modal regression
- Demonstrates broad applicability across tasks

## Abstract

We introduce a new, rigorously-formulated Bayesian meta-learning algorithm that learns a probability distribution of model parameter prior for few-shot learning. The proposed algorithm employs a gradient-based variational inference to infer the posterior of model parameters to a new task. Our algorithm can be applied to any model architecture and can be implemented in various machine learning paradigms, including regression and classification. We show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate, with state-of-the-art calibration and classification results on two few-shot classification benchmarks (Omniglot and Mini-ImageNet), and competitive results in a multi-modal task-distribution regression.