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Stochastic Gradient Descent in Continuous Time: A Central Limit Theorem

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  • Justin Sirignano
  • Konstantinos Spiliopoulos

Abstract

Stochastic gradient descent in continuous time (SGDCT) provides a computationally efficient method for the statistical learning of continuous-time models, which are widely used in science, engineering, and finance. The SGDCT algorithm follows a (noisy) descent direction along a continuous stream of data. The parameter updates occur in continuous time and satisfy a stochastic differential equation. This paper analyzes the asymptotic convergence rate of the SGDCT algorithm by proving a central limit theorem (CLT) for strongly convex objective functions and, under slightly stronger conditions, for non-convex objective functions as well. An $L^{p}$ convergence rate is also proven for the algorithm in the strongly convex case. The mathematical analysis lies at the intersection of stochastic analysis and statistical learning.

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  • Justin Sirignano & Konstantinos Spiliopoulos, 2017. "Stochastic Gradient Descent in Continuous Time: A Central Limit Theorem," Papers 1710.04273, arXiv.org, revised Jun 2019.
    • Handle: RePEc:arx:papers:1710.04273
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    References listed on IDEAS

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    3. Veretennikov, A. Yu., 1997. "On polynomial mixing bounds for stochastic differential equations," Stochastic Processes and their Applications, Elsevier, vol. 70(1), pages 115-127, October.
    4. Yacine Ait-Sahalia, 2002. "Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed-form Approximation Approach," Econometrica, Econometric Society, vol. 70(1), pages 223-262, January.
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