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• W3006493147 abstract "We introduce a novel and efficient algorithm called the stochastic approximate gradient descent (SAGD), as an alternative to the stochastic gradient descent for cases where unbiased stochastic gradients cannot be trivially obtained. Traditional methods for such problems rely on general-purpose sampling techniques such as Markov chain Monte Carlo, which typically requires manual intervention for tuning parameters and does not work efficiently in practice. Instead, SAGD makes use of the Langevin algorithm to construct stochastic gradients that are biased in finite steps but accurate asymptotically, enabling us to theoretically establish the convergence guarantee for SAGD. Inspired by our theoretical analysis, we also provide useful guidelines for its practical implementation. Finally, we show that SAGD performs well experimentally in popular statistical and machine learning problems such as the expectation-maximization algorithm and the variational autoencoders." @default.
• W3006493147 created "2020-02-24" @default.
• W3006493147 creator A5039257426 @default.
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• W3006493147 date "2020-02-13" @default.
• W3006493147 modified "2023-10-16" @default.
• W3006493147 title "Stochastic Approximate Gradient Descent via the Langevin Algorithm" @default.
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• W3006493147 doi "https://doi.org/10.48550/arxiv.2002.05519" @default.
• W3006493147 hasPublicationYear "2020" @default.
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