3 citations · 3 across the 4 of their papers we have counts for
5 papers
Network-Aware Differential Privacy
Zhou Li, Yu Zheng, Tianhao Wang +1
Differential privacy (DP) is a privacy-enhancement technology (PET) that receives prominent attention from the academia, industry, and government. One main development over the pas…
Does Differential Privacy Impact Bias in Pretrained NLP Models?
Md. Khairul Islam, Andrew Wang, Tianhao Wang +3
Differential privacy (DP) is applied when fine-tuning pre-trained large language models (LLMs) to limit leakage of training examples. While most DP research has focused on improvin…
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
Fengyu Gao, Ruida Zhou, Tianhao Wang +2
Large Language Models (LLMs) rely on the contextual information embedded in examples/demonstrations to perform in-context learning (ICL). To mitigate the risk of LLMs potentially l…
Towards Certified Unlearning for Deep Neural Networks
Binchi Zhang, Yushun Dong, Tianhao Wang +1
In the field of machine unlearning, certified unlearning has been extensively studied in convex machine learning models due to its high efficiency and strong theoretical guarantees…
Learning Interpretable Fair Representations
Tianhao Wang, Zana Buçinca, Zilin Ma
Numerous approaches have been recently proposed for learning fair representations that mitigate unfair outcomes in prediction tasks. A key motivation for these methods is that the…