Lynn Chua

According to our database, Lynn Chua authored at least 15 papers between 2014 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy.

Yangsibo Huang, Daogao Liu, Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Milad Nasr, Amer Sinha, Chiyuan Zhang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Balls-and-Bins Sampling for DP-SGD.

Lynn Chua, Badih Ghazi, Charlie Harrison, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models.

Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chulin Xie, Chiyuan Zhang

CoRR, 2024

Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning.

Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Daogao Liu, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

CoRR, 2024

How Private is DP-SGD?

Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

CoRR, 2024

Training Differentially Private Ad Prediction Models with Semi-Sensitive Features.

Lynn Chua, Qiliang Cui, Badih Ghazi, Charlie Harrison, Pritish Kamath, Walid Krichene, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash V. Varadarajan, Chiyuan Zhang

CoRR, 2024

Scalable DP-SGD: Shuffling vs. Poisson Subsampling.

Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

How Private are DP-SGD Implementations?

Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Training Differentially Private Ad Prediction Models With Semi-Sensitive Features.

Lynn Chua, Qiliang Cui, Badih Ghazi, Charlie Harrison, Pritish Kamath, Walid Krichene, Ravi Kumar, Pasin Manurangsi, Nicolas Mayoraz, Hema Venkata Krishna Giri Narra, Steffen Rendle, Amer Sinha, Avinash V. Varadarajan, Chiyuan Zhang

Proceedings of the Workshop on Data Mining for Online Advertising (AdKDD 2024) co-located with the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024), 2024

2020

Discrete and Complex Algorithms for Curves.

Lynn Chua

PhD thesis, 2020

On the Concrete Security of LWE with Small Secret.

Hao Chen, Lynn Chua, Kristin E. Lauter, Yongsoo Song

IACR Cryptol. ePrint Arch., 2020

2019

On Cycles of Pairing-Friendly Elliptic Curves.

Alessandro Chiesa, Lynn Chua, Matthew Weidner

SIAM J. Appl. Algebra Geom., 2019

Schottky algorithms: Classical meets tropical.

Lynn Chua, Mario Kummer, Bernd Sturmfels

Math. Comput., 2019

Computing Theta Functions with Julia.

Daniele Agostini, Lynn Chua

CoRR, 2019

2014

Equipopularity Classes of 132-Avoiding Permutations.

Lynn Chua, Krishanu Roy Sankar

Electron. J. Comb., 2014