Changjian Shui

Evolving Domain Generalization

Wei Wang

Gezheng Xu

Ruizhi Pu

Jiaqi Li

Fan Zhou

Changjian Shui

Charles Ling

Christian Gagné

Boyu Wang

2022-05-30

ArXiv (preprint)

doi.org

arxiv.org

On the benefits of representation regularization in invariance based domain generalization

Changjian Shui

Boyu Wang

Christian Gagné

A crucial aspect of reliable machine learning is to design a deployable system for generalizing new related but unobserved environments. Dom… (see more)ain generalization aims to alleviate such a prediction gap between the observed and unseen environments. Previous approaches commonly incorporated learning the invariant representation for achieving good empirical performance. In this paper, we reveal that merely learning the invariant representation is vulnerable to the related unseen environment. To this end, we derive a novel theoretical analysis to control the unseen test environment error in the representation learning, which highlights the importance of controlling the smoothness of representation. In practice, our analysis further inspires an efficient regularization method to improve the robustness in domain generalization. The proposed regularization is orthogonal to and can be straightforwardly adopted in existing domain generalization algorithms that ensure invariant representation learning. Empirical results show that our algorithm outperforms the base versions in various datasets and invariance criteria.

2021-12-31

Machine Learning (published)

doi.org

arxiv.org

Deep Active Learning: Unified and Principled Method for Query and Training

Changjian Shui

Fan Zhou

Christian Gagné

Boyu Wang

2020-06-02

Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (published)

proceedings.mlr.press