Continuous Meta-Learning without Tasks

However, there are several practical considerations in the choice of meta-learning algorithm which can influence the computational efficiency and overall performance of MOCA. For the experiments in this paper, we leverage two meta-learning algorithms which offer a clean Bayesian learning interpretation, relatively low-dimensional posterior statistics, recursive updates for these statistics, and computationally efficient likelihood evaluation under the posterior predictive. For regression experiments, we use ALPaCA (Harrison et al., 2018); for classification experiments, we use a novel algorithm based on similar Bayesian updates which we refer to as PCOC, for probabilistic clustering for online classification. For completeness, we offer a high level overview of these algorithms and show how they fit into the MOCA framework in the following subsections.