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Meta-learning or few-shot learning, has been successfully applied in a widerange of domains from computer vision to reinforcement learning. Among the manyframeworks proposed for meta-learning, bayesian methods are particularlyfavoured when accurate and calibrated uncertainty estimate is required. In thispaper, we investigate the similarities and disparities among two recentlypublished bayesian meta-learning methods: ALPaCA (Harrison et al. [2018]) andPACOH (Rothfuss et al. [2020]). We provide theoretical analysis as well asempirical benchmarks across synthetic and real-world dataset. While ALPaCAholds advantage in computation time by the usage of a linear kernel, generalGP-based methods provide much more flexibility and achieves better resultacross datasets when using a common kernel such as SE (Squared Exponential)kernel. The influence of different loss function choice is also discussed.

ALPaCA vs. GP-based Prior Learning: A Comparison between two Bayesian Meta-Learning Algorithms