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Paul Blomstedt

2 accepted papers

2021

Federated stochastic gradient Langevin dynamics

UAI 2021poster

Stochastic gradient MCMC methods, such as stochastic gradient Langevin dynamics (SGLD), employ fast but noisy gradient estimates to enable large-scale posterior sampling. Although we can easily extend SGLD to distributed settings, it suffers from two issues when applied to federated non-IID data. Fi…

Cited by 30SourcePDFScholar
2019

Embarrassingly Parallel MCMC using Deep Invertible Transformations

UAI 2019poster

While MCMC methods have become a main work-horse for Bayesian inference, scaling them to large distributed datasets is still a challenge. Embarrassingly parallel MCMC strategies take a divide-and-conquer stance to achieve this by writing the target posterior as a product of subposteriors, running MC…