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- W2912386695 abstract "We introduce a family of pairwise stochastic gradient estimators for gradients of expectations, which are related to the log-derivative trick, but involve pairwise interactions between samples. The simplest example of our new estimator, dubbed the fundamental trick estimator, is shown to arise from either a) introducing and approximating an integral representation based on the fundamental theorem of calculus, or b) applying the reparameterisation trick to an implicit parameterisation under infinitesimal perturbation of the parameters. From the former perspective we generalise to a reproducing kernel Hilbert space representation, giving rise to a locality parameter in the pairwise interactions mentioned above, yielding our representer trick estimator. The resulting estimators are unbiased and shown to offer an independent component of useful information in comparison with the log-derivative estimator. We provide a further novel theoretical analysis which further characterises the variance reduction afforded by the new techniques. Promising analytical and numerical examples confirm the theory and intuitions behind the new estimators." @default.
- W2912386695 created "2019-02-21" @default.
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- W2912386695 date "2019-01-31" @default.
- W2912386695 modified "2023-09-27" @default.
- W2912386695 title "New Tricks for Estimating Gradients of Expectations" @default.
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- W2912386695 doi "https://doi.org/10.48550/arxiv.1901.11311" @default.
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