Efficient Variance Reduction for Meta-learning
Abstract
Meta-learning tries to learn meta-knowledge from a large number of tasks. However, the stochastic meta-gradient can have large variance due to data sampling (from each task) and task sampling (from the whole task distribution), leading to slow convergence. In this paper, we propose a novel approach that integrates variance reduction with first-order meta-learning algorithms such as Reptile. It retains the bilevel formulation which better captures the structure of meta-learning, but does not require storing the vast number of task-specific parameters in general bilevel variance reduction methods. Theoretical results show that it has fast convergence rate due to variance reduction. Experiments on benchmark few-shot classification data sets demonstrate its effectiveness over state-of-the-art meta-learning algorithms with and without variance reduction.
BibTeX
@InProceedings{pmlr-v162-yang22g,
title = {Efficient Variance Reduction for Meta-learning},
author = {Yang, Hansi and Kwok, James},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {25070--25095},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/yang22g/yang22g.pdf},
url = {https://proceedings.mlr.press/v162/yang22g.html},
abstract = {Meta-learning tries to learn meta-knowledge from a large number of tasks. However, the stochastic meta-gradient can have large variance due to data sampling (from each task) and task sampling (from the whole task distribution), leading to slow convergence. In this paper, we propose a novel approach that integrates variance reduction with first-order meta-learning algorithms such as Reptile. It retains the bilevel formulation which better captures the structure of meta-learning, but does not require storing the vast number of task-specific parameters in general bilevel variance reduction methods. Theoretical results show that it has fast convergence rate due to variance reduction. Experiments on benchmark few-shot classification data sets demonstrate its effectiveness over state-of-the-art meta-learning algorithms with and without variance reduction.}
}