Stochastic Learning Rate Optimization in the Stochastic Approximation and Online Learning Settings

• URL: http://arxiv.org/abs/2110.10710v1
• Date: Wed, 20 Oct 2021 18:10:03 GMT
• Title: Stochastic Learning Rate Optimization in the Stochastic Approximation and Online Learning Settings
• Authors: Theodoros Mamalis, Dusan Stipanovic, Petros Voulgaris
• Abstract summary: In this work, multiplicativeity is applied to the learning rate of optimization algorithms, giving rise to learning-rate schemes. In this work, theoretical convergence results of Gradient Descent equipped with this novel learning rate scheme are presented.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, multiplicative stochasticity is applied to the learning rate of stochastic optimization algorithms, giving rise to stochastic learning-rate schemes. In-expectation theoretical convergence results of Stochastic Gradient Descent equipped with this novel stochastic learning rate scheme under the stochastic setting, as well as convergence results under the online optimization settings are provided. Empirical results consider the case of an adaptively uniformly distributed multiplicative stochasticity and include not only Stochastic Gradient Descent, but also other popular algorithms equipped with a stochastic learning rate. They demonstrate noticeable optimization performance gains, with respect to their deterministic-learning-rate versions.

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