Abstract:Within many machine learning algorithms, a fundamental problem concerns efficient calculation of an unbiased gradient wrt parameters γ for expectation-based objectives E qγ (y) [f (y)]. Most existing methods either (i) suffer from high variance, seeking help from (often) complicated variance-reduction techniques; or (ii) they only apply to reparameterizable continuous random variables and employ a reparameterization trick. To address these limitations, we propose a General and One-sample (GO) gradient that (i)… Show more
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