Optimizers are essential for model training in machine learning. However, there lacks a way to estimate their performance on out-of-distribution data. For example, would a strong in-distribution optimizer work well on out-of-distribution datasets?
This project will investigate possible ways to resolve this problem and perhaps improves model robustness in the long run.
- Knowledge of using Pytorch for deep learning studies.
- Strong math abilities.