# BOML: A Modularized Bilevel Optimization Library in Python for Meta Learning

arXiv: https://arxiv.org/abs/2009.13357 · HTML: https://mlsanity.com/paper/2009.13357/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond](https://mlsanity.com/paper/2101.11517/) (2021)
- [Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond](https://mlsanity.com/paper/2110.00455/) (2021)
- [Meta-learning in healthcare: A survey](https://mlsanity.com/paper/2308.02877/) (2023)
- [INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks](https://mlsanity.com/paper/2207.13283/) (2022)
- [DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization](https://mlsanity.com/paper/2212.02376/) (2022)
- [A Penalty-Based Method for Communication-Efficient Decentralized Bilevel Programming](https://mlsanity.com/paper/2211.04088/) (2022)
- [Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates](https://mlsanity.com/paper/2403.11687/) (2024)
- [Advancing Generalized Transfer Attack with Initialization Derived Bilevel Optimization and Dynamic Sequence Truncation](https://mlsanity.com/paper/2406.02064/) (2024)
- [DUET: Decentralized Bilevel Optimization without Lower-Level Strong Convexity](https://mlsanity.com/paper/2606.21153/) (2026)
- [Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach](https://mlsanity.com/paper/2508.07505/) (2025)

## Abstract (arXiv)

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.