Equivalence of regularization and truncated iteration for general ill-posed problems☆

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Probabilistic Model-Agnostic Meta-Learning (2018, background): “It can be shown that, for likelihoods that are Gaussian in φi, gradient descent for a fixed number of iterations using xtr i , y tr i corresponds exactly to maximum a posteriori inference under a Gaussian prior p(φi|θ) [34].”
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning (2018, background): “This is based on the connection between early stopping and regularization previously discussed in Santos (1996), which we refer the readers to for a more detailed discussion.”