I am a Ph.D. Candidate in Computer Science at the University of Maryland advised by Prof. Heng Huang, where I am currently conducting research on Trustworthy Machine Learning. My work revolves around exploring the application of this field in various areas, such as computer vision, graph neural networks, and large foundation models. I am fortunate to have the chance to collaborate with Prof. Hongyang Zhang and Prof. Aleksandar Bojchevski.

Selected Publications

De-mark: Watermark Removal in Large Language Models
Ruibo Chen*, Yihan Wu*, Junfeng Guo, Heng Huang.
In International Conference on Machine Learning, ICML 2025.

A Watermark for Order-Agnostic Language Models
Ruibo Chen*, Yihan Wu*, Yanshuo Chen, Chenxi Liu, Junfeng Guo, Heng Huang.
In International Conference on Learning Representations, ICLR 2025.

A Resilient and Accessible Distribution-Preserving Watermark for Large Language Models
Yihan Wu, Zhengmian Hu, Junfeng Guo, Hongyang Zhang, Heng Huang.
In International Conference on Machine Learning, ICML 2024.

A Law of Robustness beyond Isoperimetry
Yihan Wu, Heng Huang, Hongyang Zhang.
In International Conference on Machine Learning, ICML 2023.

Adversarial Weight Perturbation Improves Generalization in Graph Neural Network
Yihan Wu, Aleksandar Bojchevski, Heng Huang.
In Conference on Artificial Intelligence, AAAI 2023 (oral).

RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval
Yihan Wu, Hongyang Zhang, Heng Huang.
In International Conference on Machine Learning, ICML 2022.

Completing the picture: Randomized smoothing suffers from the curse of dimensionality for a large family of distributions
Yihan Wu, Aleksandar Bojchevski, Aleksei Kuvshinov, Stephan Günnemann.
In International Conference on Artificial Intelligence and Statistics, AISTATS 2021.

_{(* equal contribution. For a complete list, please visit my Google Scholar profile.)}