Monte Carlo Gradient Estimation in Machine Learning

25 Jun 2019Shakir MohamedMihaela RoscaMichael FigurnovAndriy Mnih

This paper is a broad and accessible survey of the methods we have at our disposal for Monte Carlo gradient estimation in machine learning and across the statistical sciences: the problem of computing the gradient of an expectation of a function with respect to parameters defining the distribution that is integrated; the problem of sensitivity analysis. In machine learning research, this gradient problem lies at the core of many learning problems, in supervised, unsupervised and reinforcement learning... (read more)

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