论文标题
黄褐色！二重优化变得容易：一种简单的一阶方法
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach
论文作者
论文摘要
二重优化（BO）可用于解决各种重要的机器学习问题，包括但不限于超参数优化，元学习，持续学习和增强学习。传统的BO方法需要通过与隐式分化的低级优化过程进行区分，这需要与Hessian矩阵相关的昂贵计算。最近，人们一直在寻求BO的一阶方法，但是迄今为止提出的方法对于大规模的深度学习应用程序往往是复杂且不切实际的。在这项工作中，我们提出了一种简单的一阶BO算法，仅取决于一阶梯度信息，不需要隐含的区分，并且对于大型非凸函数在深度学习中的函数是实用且有效的。我们为提出的方法提供了非注重方法分析非凸目标的固定点，并提出了表明其出色实践绩效的经验结果。
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.
