• Corpus ID: 204788825

First-Order Preconditioning via Hypergradient Descent

@article{Moskovitz2019FirstOrderPV,
  title={First-Order Preconditioning via Hypergradient Descent},
  author={Theodore H. Moskovitz and Rui Wang and Janice Lan and Sanyam Kapoor and Thomas Miconi and Jason Yosinski and Aditya Rawal},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.08461}
}
Standard gradient descent methods are susceptible to a range of issues that can impede training, such as high correlations and different scaling in parameter space.These difficulties can be addressed by second-order approaches that apply a pre-conditioning matrix to the gradient to improve convergence. Unfortunately, such algorithms typically struggle to scale to high-dimensional problems, in part because the calculation of specific preconditioners such as the inverse Hessian or Fisher… 
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