Research Statement
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As robustness and privacy are mainly concerned with data distribution shift and inference under an adversarial setting, ML generalization---a never-ending pursuit of the ML community for decades---tackles these aspects under natural distribution shifts. Thus, natural questions arise: What is the relationship between the privacy, robustness, and generalization of ML? Can we leverage the advances of one to help the other? 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 adversarial (robustness) and domain (generalization) transferability are bidirectional indicators 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 different trustworthy functionalities of ML systems.
Recent Publications
Uncovering underlying connections with robustness/privacy
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Xiaojun Xu*, Jacky Zhang*, Evelyn Ma, Hyun Ho Son, Sanmi Koyejo, Bo Li. ICML 2022
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Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability Kaizhao Liang*, Jacky Zhang*, Boxin Wang, Zhuolin Yang, Sanmi Koyejo, Bo Li. ICML 2021
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Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation Haoxiang Wang, Han Zhao, Bo Li. ICML 2021
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Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond Haoxiang Wang, Bo Li, Han Zhao. ICML 2022
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ML generalization
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Certifying Some Distributional Fairness with Subpopulation Decomposition Mintong Kang, Linyi Li, Maurice Weber, Yang Liu, Ce Zhang, Bo Li. NeurIPS 2022
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Certifying Out-of-Domain Generalization for Blackbox Functions Maurice Weber, Linyi Li, Boxin Wang, Zhikuan Zhao, Bo Li, Ce Zhang. ICML 2022
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Provable Domain Generalization via Invariant-Feature Subspace Recovery Haoxiang Wang, Haozhe Si, Bo Li, Han Zhao. ICML 2022
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Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning Haoxiang Wang, Yite Wang, Ruoyu Sun, Bo Li. CVPR 2022
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PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures Dan Hendrycks, Andy Zou, Mantas Mazeika, Leonard Tang, Bo Li, Dawn Song, Jacob Steinhardt. CVPR 2022
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What Would Jiminy Cricket Do? Towards Agents That Behave Morally Dan Hendrycks, Mantas Mazeika, Andy Zou, Sahil Patel, Christine Zhu, Jesus Navarro, Dawn Song, Bo Li, Jacob Steinhardt. NeurIPS 2021
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Ruoxi Jia, Fan Wu, Xuehui Sun, Jiacen Xu, David Dao, Bhavya Kailkhura, Ce Zhang, Bo Li, Dawn Song. CVPR 2021
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On Convergence of Nearest Neighbor Classifiers over Feature Transformations Luka Rimanic, Cedric Renggli, Bo Li, Ce Zhang. NeurIPS 2020
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Controllable Orthogonalization in Training DNNs Lei Huang, Li Liu, Fan Zhu, Diwen Wan, Zehuan Yuan, Bo Li, Ling Shao. CVPR 2020
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Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nezihe Merve Gurel, Bo Li, Ce Zhang, Costas Spanos, Dawn Song. VLDB 2019
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Towards Efficient Data Valuation Based on the Shapley Value Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Bo Li, Ce Zhang, Dawn Song, Costas Spanos. AISTATS 2019
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Lei Huang, Xianglong Liu, Bo Lang, Adams Wei Yu, Bo Li. AAAI 2017
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