Amortized Proximal Optimization

@article{Bae2022AmortizedPO,
  title={Amortized Proximal Optimization},
  author={Juhan Bae and Paul Vicol and Jeff Z. HaoChen and Roger B. Grosse},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.00089}
}
We propose a framework for online meta-optimization of parameters that govern optimization, called Amortized Proximal Optimization (APO). We first interpret various existing neural network optimizers as approximate stochastic proximal point methods which trade off the current-batch loss with proximity terms in both function space and weight space. The idea behind APO is to amortize the minimization of the proximal point objective by meta-learning the parameters of an update rule. We show how… 

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