Rover Descent: Learning to optimize by learning to navigate on prototypical loss surfaces

22 Jan 2018Louis FauryFlavian Vasile

Learning to optimize - the idea that we can learn from data algorithms that optimize a numerical criterion - has recently been at the heart of a growing number of research efforts. One of the most challenging issues within this approach is to learn a policy that is able to optimize over classes of functions that are fairly different from the ones that it was trained on... (read more)

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