@inproceedings{897f86f8f43041898f420da80c0fb26d,
title = "Stochastic Learning Rate Optimization in the Stochastic Approximation and Online Learning Settings",
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 equipped with a stochastic learning rate.",
author = "Theodoros Mamalis and Dusan Stipanovic and Voulgaris, {Petros G}",
note = "Publisher Copyright: {\textcopyright} 2022 American Automatic Control Council.; 2022 American Control Conference, ACC 2022 ; Conference date: 08-06-2022 Through 10-06-2022",
year = "2022",
doi = "10.23919/ACC53348.2022.9867565",
language = "English (US)",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4286--4291",
booktitle = "2022 American Control Conference, ACC 2022",
address = "United States",
}