7 citations · 7 across the 1 of their papers we have counts for
10 papers
Differentially Private Deep Learning with Smooth Sensitivity
Lichao Sun, Yingbo Zhou, Philip S. Yu +1
Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice. One approach to study these concerns is th…
Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT
Lichao Sun, Kazuma Hashimoto, Wenpeng Yin +4
There is an increasing amount of literature that claims the brittleness of deep neural networks in dealing with adversarial examples that are created maliciously. It is unclear, ho…
Not Just Cloud Privacy: Protecting Client Privacy in Teacher-Student Learning
Lichao Sun, Ji Wang, Philip S. Yu +1
Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice. One recent popular approach to study these…
Private Deep Learning with Teacher Ensembles
Lichao Sun, Yingbo Zhou, Ji Wang +4
Privacy-preserving deep learning is crucial for deploying deep neural network based solutions, especially when the model works on data that contains sensitive information. Most pri…
Influence Maximization with Spontaneous User Adoption
Lichao Sun, Albert Chen, Philip S. Yu +1
We incorporate self activation into influence propagation and propose the self-activation independent cascade (SAIC) model: nodes may be self activated besides being selected as se…
Private Model Compression via Knowledge Distillation
Ji Wang, Weidong Bao, Lichao Sun +3
The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs noto…