Stochastic Learning Rate With Memory: Optimization in the Stochastic Approximation and Online Learning Settings-Reference-Cited by-同舟云学术

Stochastic Learning Rate With Memory: Optimization in the Stochastic Approximation and Online Learning Settings

Author:

Mamalis Theodoros¹^ORCID,Stipanovic Dusan²^ORCID,Voulgaris Petros³^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, University of Illinois at Urbana–Champaign, Urbana, IL, USA

2. Coordinated Science Laboratory, University of Illinos at Urbana–Champaign, Urbana, IL, USA

3. Department of Mechanical Engineering, University of Nevada, Reno, NV, USA

Funder

National Robotics Initiative Grant titled “NRI: FND:COLLAB: Multi-Vehicle Systems for Collecting Shadow-Free Imagery in Precision Agriculture”

USDA National Institute of Food and Agriculture

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Control and Optimization,Control and Systems Engineering

Link

Reference21 articles.

1. Non-asymptotic analysis of stochastic approximation algorithms for machine learning;moulines;Proc NeurIPS,2011

3. Online convex programming and generalized infinitesimal gradient ascent;zinkevich;Proc ICML,2003

4. Optimization in the stochastic approximation and online learning settings using stochastic learning rates;mamalis;Proc ACC,2022

5. Accelerated almost-sure convergence rates for nonconvex stochastic gradient descent using stochastic learning rates;mamalis;arXiv 0706 1234,2021

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2. Performance Analysis of Large Scale Machine Learning Optimization Algorithms;2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT);2023-04-08