  Meta-learning or few-shot learning, has been successfully applied in a wide
range of domains from computer vision to reinforcement learning. Among the many
frameworks proposed for meta-learning, bayesian methods are particularly
favoured when accurate and calibrated uncertainty estimate is required. In this
paper, we investigate the similarities and disparities among two recently
published bayesian meta-learning methods: ALPaCA (Harrison et al. [2018]) and
PACOH (Rothfuss et al. [2020]). We provide theoretical analysis as well as
empirical benchmarks across synthetic and real-world dataset. While ALPaCA
holds advantage in computation time by the usage of a linear kernel, general
GP-based methods provide much more flexibility and achieves better result
across datasets when using a common kernel such as SE (Squared Exponential)
kernel. The influence of different loss function choice is also discussed.
