Research Area:  Machine Learning

Abstract:

Online meta-learning has recently emerged as a marriage between batch meta-learning and online learning, for achieving the capability of quick adaptation on new tasks in a lifelong manner. However, most existing approaches focus on the restrictive setting where the distribution of the online tasks remains fixed with known task boundaries. In this work we relax these assumptions and propose a novel algorithm for task-agnostic online meta-learning in non-stationary environments. More specifically, we first propose two simple but effective detection mechanisms of task switches and distribution shift based on empirical observations, which serve as a key building block for more elegant online model updates in our algorithm: the task switch detection mechanism allows reusing of the best model available for the current task at hand, and the distribution shift detection mechanism differentiates the meta model update so as to preserve the knowledge for in-distribution tasks and quickly learn the new knowledge for out-of-distribution tasks. Motivated by the recent advance in online learning, our online meta model updates are based only on the current data, which eliminates the need of storing previous data as required in most existing methods. This crucial choice is also well supported by our theoretical analysis of dynamic regret in online meta-learning, where a sublinear regret can be achieved by updating the meta model at each round using the current data only. Empirical studies on three different benchmarks clearly demonstrate the significant advantage of our algorithm over related baseline approaches.

Keywords:  
Online meta-learning
Domain shift
Dynamic regret
Out of distribution detection

Author(s) Name:  Daouda Sow, Sen Lin, Yingbin Liang, Junshan Zhang

Publisher name:  Open Review

DOI:  

Volume Information:  

Paper Link:   https://openreview.net/forum?id=jsZ8PDQOVU