Guided evolutionary strategies: Augmenting random search with surrogate gradients

ICLR 2019 Niru MaheswaranathanLuke MetzGeorge TuckerDami ChoiJascha Sohl-Dickstein

Many applications in machine learning require optimizing a function whose true gradient is unknown, but where surrogate gradient information (directions that may be correlated with, but not necessarily identical to, the true gradient) is available instead. This arises when an approximate gradient is easier to compute than the full gradient (e.g. in meta-learning or unrolled optimization), or when a true gradient is intractable and is replaced with a surrogate (e.g. in certain reinforcement learning applications, or when using synthetic gradients)... (read more)

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