Kalman Gradient Descent: Adaptive Variance Reduction in Stochastic Optimization

James Vuckovic

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arXiv · 1810.12273

Kalman Gradient Descent: Adaptive Variance Reduction in Stochastic Optimization

James Vuckovic

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Abstract

We introduce Kalman Gradient Descent, a stochastic optimization algorithm that uses Kalman filtering to adaptively reduce gradient variance in stochastic gradient descent by filtering the gradient estimates. We present both a theoretical analysis of convergence in a non-convex setting and experimental results which demonstrate improved performance on a variety of machine learning areas including neural networks and black box variational inference. We also present a distributed version of our algorithm that enables large-dimensional optimization, and we extend our algorithm to SGD with momentum and RMSProp.

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James Vuckovic. 2018-10-29. Kalman Gradient Descent: Adaptive Variance Reduction in Stochastic Optimization. https://arxiv.org/abs/1810.12273

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