The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling

author: Michael Betancourt, Department of Statistics, University of Warwick
published: Dec. 5, 2015,   recorded: October 2015,   views: 1650
Categories

See Also:

Download slides icon Download slides: icml2015_betancourt_data_subsampling_01.pdf (1.7┬áMB)


Help icon Streaming Video Help

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

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.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: