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- W4293494777 abstract "We show a connection between the Fourier spectrum of Boolean functions and the REINFORCE gradient estimator for binary latent variable models. We show that REINFORCE estimates (up to a factor) the degree-1 Fourier coefficients of a Boolean function. Using this connection we offer a new perspective on variance reduction in gradient estimation for latent variable models: namely, that variance reduction involves eliminating or reducing Fourier coefficients that do not have degree 1. We then use this connection to develop low-variance unbiased gradient estimators for binary latent variable models such as sigmoid belief networks. The estimator is based upon properties of the noise operator from Boolean Fourier theory and involves a sample-dependent baseline added to the REINFORCE estimator in a way that keeps the estimator unbiased. The baseline can be plugged into existing gradient estimators for further variance reduction." @default.
- W4293494777 created "2022-08-29" @default.
- W4293494777 creator A5079743569 @default.
- W4293494777 date "2018-08-12" @default.
- W4293494777 modified "2023-09-27" @default.
- W4293494777 title "A Fourier View of REINFORCE" @default.
- W4293494777 doi "https://doi.org/10.48550/arxiv.1808.03953" @default.
- W4293494777 hasPublicationYear "2018" @default.
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