  Meta-learning methods typically learn tasks under the assumption that all
tasks are equally important. However, this assumption is often not valid. In
real-world applications, tasks can vary both in their importance during
different training stages and in whether they contain noisy labeled data or
not, making a uniform approach suboptimal. To address these issues, we propose
the Data-Efficient and Robust Task Selection (DERTS) algorithm, which can be
incorporated into both gradient and metric-based meta-learning algorithms.
DERTS selects weighted subsets of tasks from task pools by minimizing the
approximation error of the full gradient of task pools in the meta-training
stage. The selected tasks are efficient for rapid training and robust towards
noisy label scenarios. Unlike existing algorithms, DERTS does not require any
architecture modification for training and can handle noisy label data in both
the support and query sets. Analysis of DERTS shows that the algorithm follows
similar training dynamics as learning on the full task pools. Experiments show
that DERTS outperforms existing sampling strategies for meta-learning on both
gradient-based and metric-based meta-learning algorithms in limited data budget
and noisy task settings.
