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arXiv · 2205.12549

Learning from time-dependent streaming data with online stochastic algorithms

Antoine Godichon-Baggioni·Nicklas Werge·Olivier Wintenberger
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Abstract

This paper addresses stochastic optimization in a streaming setting with time-dependent and biased gradient estimates. We analyze several first-order methods, including Stochastic Gradient Descent (SGD), mini-batch SGD, and time-varying mini-batch SGD, along with their Polyak-Ruppert averages. Our non-asymptotic analysis establishes novel heuristics that link dependence, biases, and convexity levels, enabling accelerated convergence. Specifically, our findings demonstrate that (i) time-varying mini-batch SGD methods have the capability to break long- and short-range dependence structures, (ii) biased SGD methods can achieve comparable performance to their unbiased counterparts, and (iii) incorporating Polyak-Ruppert averaging can accelerate the convergence of the stochastic optimization algorithms. To validate our theoretical findings, we conduct a series of experiments using both simulated and real-life time-dependent data.

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Antoine Godichon-Baggioni, Nicklas Werge, Olivier Wintenberger. 2022-05-25. Learning from time-dependent streaming data with online stochastic algorithms. https://arxiv.org/abs/2205.12549

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