- Scalable MCMC for Mixed Membership Stochastic Blockmodels
- JMLR Workshop and Conference Proceedings
- Pages (from-to)
- Document type
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
We propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in mixed-membership stochastic blockmodels (MMSB). Our algorithm is based on the stochastic gradient Riemannian Langevin sampler and achieves both faster speed and higher accuracy at every iteration than the current state-of-the-art algorithm based on stochastic variational inference. In addition we develop an approximation that can handle models that entertain a very large number of communities. The experimental results show that SG-MCMC strictly dominates competing algorithms in all cases.
- Proceedings title: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics
Editors: A. Gretton, C.C. Robert
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