Boml: A Modularized Bilevel Optimization Library In Python For Meta Learning
Published in IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2021
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/dutmedia-lab/BOML.
Recommended citation: Yaohua Liu, Risheng Liu. Boml: A Modularized Bilevel Optimization Library In Python For Meta Learning[C]. IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2021, Best Open Source Project Awards, Oral.
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