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@InProceedings{NeurIPS2022-Liu,
  author = {Bo Liu and Mao Ye and Stephen Wright and Peter Stone and Qiang Liu},
  title = {BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach},
  booktitle = {Conference on Neural Information Processing Systems, 2022},
  location = {New Orleans, LA},
  month = {December},
  year = {2022},
  abstract = {
  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.
  },
}