Domain Generalization and Other ML Utilities

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Research Statement

As robustness and privacy are mainly concerned about data distribution shift and inference under an adversarial setting, ML generalization---an ever-ending pursuit of the ML community for decades---tackles these aspects appearing in a natural setting. Thus, natural questions arise: What is the relationship between the privacy, robustness, and generalization of learning algorithms? Can we leverage the advances of one to help address another? Is there a tradeoff between robustness, privacy, and domain generalization?

Towards improving ML generalization, we focus on two perspectives: (1) uncovering the underlying connections between ML robustness, privacy, and generalization; (2) enabling one based on the advances of the other. For instance, our work has proved that the adversarial (robustness) and domain (generalization) transferability is a bidirectional indicator for each other, which has great implications for a range of applications such as model selection. This line of research provides the potential to further tighten the generalization of different learning systems based on their robustness or privacy properties.