BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach
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Abstract (arXiv)
Bilevel optimization (BO) is useful for solving a variety of important machine learning problems including but not limited to hyperparameter optimization, meta-learning, continual learning, and reinforcement learning. Conventional BO methods need to differentiate through the low-level optimization process with implicit differentiation, which requires expensive calculations related to the Hessian matrix. There has been a recent quest for first-order methods for BO, but the methods proposed to date tend to be complicated and impractical for large-scale deep learning applications. In this work, we propose a simple first-order BO algorithm that depends only on first-order gradient information, requires no implicit differentiation, and is practical and efficient for large-scale non-convex functions in deep learning. We provide non-asymptotic convergence analysis of the proposed method to stationary points for non-convex objectives and present empirical results that show its superior practical performance.
Key facts. Cited by 103 papers in the map (data through 13 Oct 2026). Builds on 32 of its 55 references that are in the map. First posted on arXiv 19 Sep 2022.
Builds on
32 of 55 references.
References · 32
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Citing papers per year: 2022: 1; 2023: 21; 2024: 36; 2025: 27; 2026: 18.
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