scientific article; zbMATH DE number 7626754
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Publication:5053256
Yuanhan Hu, Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu
Publication date: 6 December 2022
Full work available at URL: https://arxiv.org/abs/2007.00590
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
convergence ratedecentralized algorithmsLangevin dynamicsWasserstein distancestochastic gradientHamiltonian Monte Carloheavy-ball methodmomentum accelerationdecentralized Bayesian inference
Related Items (3)
The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems ⋮ Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo ⋮ Distributed event-triggered unadjusted Langevin algorithm for Bayesian learning
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