TY - GEN
T1 - Variational boosting
T2 - 34th International Conference on Machine Learning, ICML 2017
AU - Miller, Andrew C.
AU - Foti, Nicholas J.
AU - Adams, Ryan P.
N1 - Funding Information:
The authors would like to thank Arjumand Masood, Mike Hughes, and Finale Doshi-Velez for helpful feedback. ACM is supported by the Applied Mathematics Program within the Office of Science Advanced Scientific Computing Research of the U.S. Department of Energy under contract No. DE-ACO2-05CH11231. NJF is supported by a Washington Research Foundation Innovation Postdoctoral Fellowship in Neuroengineering and Data Science. RPA is supported by NSF IIS-1421780 and the Alfred P. Sloan Foundation.
Publisher Copyright:
© 2017 by the author(s).
PY - 2017
Y1 - 2017
N2 - We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, variational boosting, iteratively refines an existing variational approximation by solving a sequence of optimization problems, allowing a trade-off between computation time and accuracy. We expand the variational approximating class by incorporating additional covariance structure and by introducing new components to form a mixture. We apply variational boosting to synthetic and real statistical models, and show that the resulting posterior inferences compare favorably to existing variational algorithms.
AB - We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, variational boosting, iteratively refines an existing variational approximation by solving a sequence of optimization problems, allowing a trade-off between computation time and accuracy. We expand the variational approximating class by incorporating additional covariance structure and by introducing new components to form a mixture. We apply variational boosting to synthetic and real statistical models, and show that the resulting posterior inferences compare favorably to existing variational algorithms.
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M3 - Conference contribution
AN - SCOPUS:85048472697
T3 - 34th International Conference on Machine Learning, ICML 2017
SP - 3732
EP - 3747
BT - 34th International Conference on Machine Learning, ICML 2017
PB - International Machine Learning Society (IMLS)
Y2 - 6 August 2017 through 11 August 2017
ER -