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  • 2105.13059v3

    Submitted manuscript, 911 KB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

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Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC

Research output: Working paper › Preprint

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Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC. / Coullon, Jeremie; South, Leah; Nemeth, Christopher.
Arxiv, 2021.

Research output: Working paper › Preprint

Harvard

Coullon, J, South, L & Nemeth, C 2021 'Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC' Arxiv. <https://arxiv.org/abs/2105.13059>

APA

Coullon, J., South, L., & Nemeth, C. (2021). Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC. Arxiv. https://arxiv.org/abs/2105.13059

Vancouver

Coullon J, South L, Nemeth C. Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC. Arxiv. 2021 May 27.

Author

Coullon, Jeremie ; South, Leah ; Nemeth, Christopher. / Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC. Arxiv, 2021.

Bibtex

@techreport{1a9a4ae3557c41c3ae33c18637405cf9,
title = "Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC",
abstract = "Stochastic gradient Markov chain Monte Carlo (SGMCMC) is a popular class of algorithms for scalable Bayesian inference. However, these algorithms include hyperparameters such as step size or batch size that influence the accuracy of estimators based on the obtained posterior samples. As a result, these hyperparameters must be tuned by the practitioner and currently no principled and automated way to tune them exists. Standard MCMC tuning methods based on acceptance rates cannot be used for SGMCMC, thus requiring alternative tools and diagnostics. We propose a novel bandit-based algorithm that tunes the SGMCMC hyperparameters by minimizing the Stein discrepancy between the true posterior and its Monte Carlo approximation. We provide theoretical results supporting this approach and assess various Stein-based discrepancies. We support our results with experiments on both simulated and real datasets, and find that this method is practical for a wide range of applications.",
keywords = "stat.CO, stat.ME, stat.ML",
author = "Jeremie Coullon and Leah South and Christopher Nemeth",
year = "2021",
month = may,
day = "27",
language = "English",
publisher = "Arxiv",
type = "WorkingPaper",
institution = "Arxiv",

}

RIS

TY - UNPB

T1 - Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC

AU - Coullon, Jeremie

AU - South, Leah

AU - Nemeth, Christopher

PY - 2021/5/27

Y1 - 2021/5/27

N2 - Stochastic gradient Markov chain Monte Carlo (SGMCMC) is a popular class of algorithms for scalable Bayesian inference. However, these algorithms include hyperparameters such as step size or batch size that influence the accuracy of estimators based on the obtained posterior samples. As a result, these hyperparameters must be tuned by the practitioner and currently no principled and automated way to tune them exists. Standard MCMC tuning methods based on acceptance rates cannot be used for SGMCMC, thus requiring alternative tools and diagnostics. We propose a novel bandit-based algorithm that tunes the SGMCMC hyperparameters by minimizing the Stein discrepancy between the true posterior and its Monte Carlo approximation. We provide theoretical results supporting this approach and assess various Stein-based discrepancies. We support our results with experiments on both simulated and real datasets, and find that this method is practical for a wide range of applications.

AB - Stochastic gradient Markov chain Monte Carlo (SGMCMC) is a popular class of algorithms for scalable Bayesian inference. However, these algorithms include hyperparameters such as step size or batch size that influence the accuracy of estimators based on the obtained posterior samples. As a result, these hyperparameters must be tuned by the practitioner and currently no principled and automated way to tune them exists. Standard MCMC tuning methods based on acceptance rates cannot be used for SGMCMC, thus requiring alternative tools and diagnostics. We propose a novel bandit-based algorithm that tunes the SGMCMC hyperparameters by minimizing the Stein discrepancy between the true posterior and its Monte Carlo approximation. We provide theoretical results supporting this approach and assess various Stein-based discrepancies. We support our results with experiments on both simulated and real datasets, and find that this method is practical for a wide range of applications.

KW - stat.CO

KW - stat.ME

KW - stat.ML

M3 - Preprint

BT - Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC

PB - Arxiv

ER -