Ruihan Wu (吴睿涵)

I am currently a postdoctoral researcher at the University of California, San Diego, fortunately advised by Kamalika Chaudhuri. My research interests mainly lie in trustworthy machine learning. My recent interests are privacy-preserving machine learning, machine learning safety, and the practice of online learning and bandits. 

I received my Ph.D. from Cornell University, where I was very fortunate to be advised by Kilian Q. Weinberger. Prior to that, I received my B.E. in computer science from Yao Class at Tsinghua University. 

Email: ruw076 [at] ucsd [dot] edu or rw565 [at] cornell [dot] edu

[Google Scholar][LinkedIn][Github][CV]

Pre-prints

Online Feature Updates Improve Online (Generalized) Label Shift Adaptation [pdf]

Ruihan Wu*, Siddhartha Datta*, Yi Su, Dheeraj Baby, Yu-Xiang Wang, Kilian Q Weinberger. ArXiv 2024.

Better Membership Inference Privacy Measurement through Discrepancy [pdf]

Ruihan Wu*, Pengrun Huang*, Kamalika Chaudhuri. ArXiv 2024.

Large Scale Knowledge Washing [pdf]

Yu Wang, Ruihan Wu, Zexue He, Xiusi Chen, Julian McAuley. ArXiv 2024.

Large-Scale Public Data Improves Differentially Private Image Generation Quality [pdf]

Ruihan Wu, Chuan Guo, Kamalika Chaudhuri. ArXiv 2023. 

Publications and Manuscripts

Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning [pdf] [code]

Ruihan Wu*, Xiangyu Chen*, Chuan Guo, Kilian Q Weinberger. Conference on Uncertainty in Artificial Intelligence (UAI), 2023. 

Does Label Differential Privacy Prevent Label Inference Attacks? [pdf] [code]

Ruihan Wu*, Jin Peng Zhou*, Kilian Q Weinberger, Chuan Guo. International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. 

Differentially Private Multi-Party Data Release for Linear Regression [pdf] [code][video]

Ruihan Wu, Xin Yang, Yuanshun Yao, Jiankai Sun, Tianyi Liu, Kilian Q Weinberger, Chong Wang. Conference on Uncertainty in Artificial Intelligence (UAI), 2022. 

Online Adaptation to Label Distribution Shift [pdf] [code][video]

Ruihan Wu, Chuan Guo, Yi Su, Kilian Q. Weinberger. Advances of Neural Information Processing (NeurIPS), 2021. 

Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems [pdf] [code]

Ruihan Wu, Chuan Guo, Awni Hannun, Laurens van der Maaten. Advances of Neural Information Processing (NeurIPS), 2021. 

Correlator Convolutional Neural Networks as an Interpretable Architecture for Image-like Quantum Matter Data [pdf]

Cole Miles, Annabelle Bohrdt, Ruihan Wu, Christie Chiu, Muqing Xu, Geoffrey Ji, Markus Greiner, Kilian Q. Weinberger, Eugene Demler, Eun-Ah Kim. Nature Communications, 2021. 

Making Paper Reviewing Robust to Bid Manipulation Attacks [pdf] [code][video]

Ruihan Wu*, Chuan Guo*, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger. International Conference on Machine Learning (ICML), 2021. 

On Hiding Neural Networks Inside Neural Networks [pdf] [code]

Chuan Guo*, Ruihan Wu*, Kilian Q. Weinberger. ArXiv, 2020.

Scalable Lattice Influence Maximization [pdf]

Wei Chen*, Ruihan Wu*, Zheng Yu*. IEEE Transactions on Computational Social Systems (TCSS), 2020. 

Product Kernel Interpolation for Scalable Gaussian Processes  [pdf]

Jake Gardner, Geoff Peiss, Ruihan Wu, Kilian Q. Weinberger, Andrew G. Wilson. International Conference on Artificial Intelligence and Statistics (AISTATS), 2018. 

Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes [pdf]

 Lunjia Hu, Ruihan Wu, Tianhong Li, Liwei Wang. Conference on Learning Theory (COLT), 2017.  

Talks 

Women in Data Science, February 2024.

Google Differential Privacy for ML Seminar, September 2022. [link]

TrustML Young Scientist Seminar, October 2022. [link]

Selected Awards 

LinkedIn PhD Award, 2022

National Scholarship (top 2% in Tsinghua University), 2017

Gold Medal in Chinese Mathematical Olympiad , 2013