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.


Recent Publications

Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation

​Haoxiang Wang, Han Zhao, Bo Li.

ICML 2021

 

Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability

Kaizhao Liang*, Jacky Zhang*, Boxin Wang, Zhuolin Yang, Sanmi Koyejo, Bo Li.

ICML 2021

 

Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?

Ruoxi Jia, Fan Wu, Xuehui Sun, Jiacen Xu, David Dao, Bhavya Kailkhura, Ce Zhang, Bo Li, Dawn Song.

CVPR 2021

 

Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing

Maurice Weber, Nana Liu, Bo Li, Ce Zhang, Zhikuan Zhao.

npj Quantum Information 2021

 

Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation

Wenhao Ding, Baiming Chen, Bo Li, Ji Eun Kim, Ding Zhao.

ICRA 2021