  The online meta-learning framework is designed for the continual lifelong
learning setting. It bridges two fields: meta-learning which tries to extract
prior knowledge from past tasks for fast learning of future tasks, and
online-learning which deals with the sequential setting where problems are
revealed one by one. In this paper, we generalize the original framework from
convex to non-convex setting, and introduce the local regret as the alternative
performance measure. We then apply this framework to stochastic settings, and
show theoretically that it enjoys a logarithmic local regret, and is robust to
any hyperparameter initialization. The empirical test on a real-world task
demonstrates its superiority compared with traditional methods.
