Learning to Optimize with Reinforcement Learning


This post explores the potential of learning the algorithms that power machine learning instead of laboriously hand crafting them. An important advantage in the case of optimization algorithms might be that while they are currently devised under the assumption of convexity they are applied to non-convex objective functions; learning the optimization algorithm under the same setting as it will actually be used in practice could significantly boost performance.


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