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Global-Local Regularization Via Distributional Robustness

This repository contains the Pytorch implementation of Global-Local Regularization Via Distributional Robustness.

<a href="url"><img src="GLOT.png" align="center" ></a>

If you find our code useful in your research, please cite:

@article{phan2022global,
  title={Global-Local Regularization Via Distributional Robustness},
  author={Phan, Hoang and Le, Trung and Phung, Trung and Bui, Tuan Anh and Ho, Nhat and Phung, Dinh},
  journal={International Conference on Artificial Intelligence and Statistics (AISTATS), 2023},
  year={2022}
}

Our implementation consists of 4 subexperiments:

• Domain adaptation
• Domain generalization
• Semi supervised learning
• Adversarial machine learning

Reproducing:

Please refer to the bash script (*.sh) in each experiment to reproduce the results

For the running time of our approach, please refer this kernel in Kaggle as we want to utilize the cloud computing service for a fair comparision.