Improving algorithmic performance using stochastic learning rates: In-expectation and almost-surely stochastic approximation, and online learning, results and applications

Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Mamalis, Theodoros

Contributors dc:contributor

• Voulgaris, Petros
• Stipanovic, Dusan
• Liberzon, Daniel
• Subhonmesh, Bose

• Machine Learning
• Artificial Intelligence
• Optimization Algorithms
• Minimization
• Stochastic Systems
• Training Data
• Testing Data
• Upper Bound
• Empirical Loss Measurement
• Learning Rate
• Loss Function
• Convergence

Statement dc:rights

• Copyright 2024 Theodoros Mamalis

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/124240

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/124240