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Poster
HOGWILD!-Gibbs can be PanAccurate
Constantinos Daskalakis · Nishanth Dikkala · Siddhartha Jayanti

Wed Dec 05 02:00 PM -- 04:00 PM (PST) @ Room 210 #8
in Wed Poster Session B »
Asynchronous Gibbs sampling has been recently shown to be fast-mixing and an accurate method for estimating probabilities of events on a small number of variables of a graphical model satisfying Dobrushin's condition~\cite{DeSaOR16}. We investigate whether it can be used to accurately estimate expectations of functions of {\em all the variables} of the model. Under the same condition, we show that the synchronous (sequential) and asynchronous Gibbs samplers can be coupled so that the expected Hamming distance between their (multivariate) samples remains bounded by $O(\tau \log n),$ where $n$ is the number of variables in the graphical model, and $\tau$ is a measure of the asynchronicity. A similar bound holds for any constant power of the Hamming distance. Hence, the expectation of any function that is Lipschitz with respect to a power of the Hamming distance, can be estimated with a bias that grows logarithmically in $n$. Going beyond Lipschitz functions, we consider the bias arising from asynchronicity in estimating the expectation of polynomial functions of all variables in the model. Using recent concentration of measure results~\cite{DaskalakisDK17,GheissariLP17,GotzeSS18}, we show that the bias introduced by the asynchronicity is of smaller order than the standard deviation of the function value already present in the true model. We perform experiments on a multi-processor machine to empirically illustrate our theoretical findings.
Keywords: MCMC · Graphical Models · Topic Models · Statistical Physics of Learning · Distributed Inference

Author Information

Constantinos Daskalakis (MIT)
Nishanth Dikkala (MIT)
Siddhartha Jayanti (MIT)

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