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We propose a Las Vegas transformation of Markov Chain Monte Carlo (MCMC)estimators of Restricted Boltzmann Machines (RBMs). We denote our approachMarkov Chain Las Vegas (MCLV). MCLV gives statistical guarantees in exchangefor random running times. MCLV uses a stopping set built from the training dataand has maximum number of Markov chain steps K (referred as MCLV-K). We presenta MCLV-K gradient estimator (LVS-K) for RBMs and explore the correspondence anddifferences between LVS-K and Contrastive Divergence (CD-K), with LVS-Ksignificantly outperforming CD-K training RBMs over the MNIST dataset,indicating MCLV to be a promising direction in learning generative models.

From Monte Carlo to Las Vegas: Improving Restricted Boltzmann Machine Training Through Stopping Sets