Meta-learning (a.k.a. learning to learn) has recently emerged as a promising paradigm for a variety of applications. There are now many meta-learning methods, each focusing on different modeling aspects of base and meta learners, but all can be (re)formulated as specific bilevel optimization problems. This work presents BOML, a modularized optimization library that unifies several meta-learning algorithms into a common bilevel optimization framework. It provides a hierarchical optimization pipeline together with a variety of iteration modules, which can be used to solve the mainstream categories of meta-learning methods, such as meta-feature-based and meta-initialization-based formulations. The library is written in Python and is available at https://github.com/dut-media-lab/BOML.
@article{arxiv.2009.13357,
title = {BOML: A Modularized Bilevel Optimization Library in Python for Meta Learning},
author = {Yaohua Liu and Risheng Liu},
journal= {arXiv preprint arXiv:2009.13357},
year = {2020}
}