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• W2947548899 abstract "We consider the problem of sampling from a target distribution, which is emph {not necessarily logconcave}, in the context of empirical risk minimization and stochastic optimization as presented in Raginsky et al. (2017). Non-asymptotic analysis results are established in the $L^1$-Wasserstein distance for the behaviour of Stochastic Gradient Langevin Dynamics (SGLD) algorithms. We allow the estimation of gradients to be performed even in the presence of emph{dependent} data streams. Our convergence estimates are sharper and emph{uniform} in the number of iterations, in contrast to those in previous studies." @default.
• W2947548899 created "2019-06-07" @default.
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• W2947548899 date "2019-05-30" @default.
• W2947548899 modified "2023-10-06" @default.
• W2947548899 title "On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case" @default.
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• W2947548899 doi "https://doi.org/10.48550/arxiv.1905.13142" @default.
• W2947548899 hasPublicationYear "2019" @default.
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