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

arXiv: https://arxiv.org/abs/2010.07994 · HTML: https://mlsanity.com/paper/2010.07994/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Bayesian Meta-Learning Through Variational Gaussian Processes](https://mlsanity.com/paper/2110.11044/) (2021)
- [Meta-Learning Conjugate Priors for Few-Shot Bayesian Optimization](https://mlsanity.com/paper/2101.00729/) (2021)

## Abstract (arXiv)

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.