The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling
published: Dec. 5, 2015, recorded: October 2015, views: 1666
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Leveraging the coherent exploration of Hamiltonian flow, Hamiltonian Monte Carlo produces computationally efficient Monte Carlo estimators, even with respect to complex and high-dimensional target distributions. When confronted with data-intensive applications, however, the algorithm may be too expensive to implement, leaving us to consider the utility of approximations such as data subsampling. In this paper I demonstrate how data subsampling fundamentally compromises the scalability of Hamiltonian Monte Carlo.
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