  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.
