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- W4226262712 abstract "Recently, Stochastic Variational Inference (SVI) has been increasingly attractive thanks to its ability to find good posterior approximations of probabilistic models. It optimizes the variational objective with stochastic optimization, following noisy estimates of the natural gradient. However, almost all the state-of-the-art SVI algorithms are based on first-order optimization algorithm and often suffer from poor convergence rate. In this paper, we bridge the gap between second-order methods and stochastic variational inference by proposing a second-order based stochastic variational inference approach. In particular, firstly we derive the Hessian matrix of the variational objective. Then we devise two numerical schemes to implement second-order SVI efficiently. Thorough empirical evaluations are investigated on both synthetic and real dataset to backup both the effectiveness and efficiency of the proposed approach." @default.
- W4226262712 created "2022-05-05" @default.
- W4226262712 creator A5025146861 @default.
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- W4226262712 date "2022-03-14" @default.
- W4226262712 modified "2023-09-26" @default.
- W4226262712 title "Accelerating Stochastic Probabilistic Inference" @default.
- W4226262712 doi "https://doi.org/10.48550/arxiv.2203.07585" @default.
- W4226262712 hasPublicationYear "2022" @default.
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