Date of Award
6-10-2021
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2021.
Department
Computer Science and Engineering
First Advisor
Zhiqiang Tao
Abstract
With the increasing size and complexity of machine learning datasets, obtaining highly performing prediction models in various tasks has become increasingly difficult. In particular, the processs of hyperparameter optimization (HPO) contributes a significant portion of this cost. This work examines a specific graph-machine learning model, graph convolutional networks (GCN), to derive a hyperparameter configuration with optimal performance across a variety of datasets. We motivate our configuration theoretically and validate it empirically through comprehensive experimentation. We find that for GCN semi-supervised classification tasks, our configuration performs nearly optimally when compared against traditional HPO while only requiring a fraction of the budget. We further propose using this configuration to warm-start subsequent HPO as a means of accelerating its convergence.
Recommended Citation
Ligman, Drew, "Improved Hyperparameter Tuning for Graph Learning with Warm-Start Configuration" (2021). Computer Science and Engineering Senior Theses. 197.
https://scholarcommons.scu.edu/cseng_senior/197