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We introduce a new and rigorously-formulated PAC-Bayes meta-learningalgorithm that solves few-shot learning. Our proposed method extends thePAC-Bayes framework from a single task setting to the meta-learning multipletask setting to upper-bound the error evaluated on any, even unseen, tasks andsamples. We also propose a generative-based approach to estimate the posteriorof task-specific model parameters more expressively compared to the usualassumption based on a multivariate normal distribution with a diagonalcovariance matrix. We show that the models trained with our proposedmeta-learning algorithm are well calibrated and accurate, with state-of-the-artcalibration and classification results on few-shot classification(mini-ImageNet and tiered-ImageNet) and regression (multi-modaltask-distribution regression) benchmarks.

PAC-Bayes meta-learning with implicit task-specific posteriors