2 citations · 2 across the 4 of their papers we have counts for
4 papers
On the accuracy and efficiency of group-wise clipping in differentially private optimization
Zhiqi Bu, Ruixuan Liu, Yu-Xiang Wang +2
Recent advances have substantially improved the accuracy, memory cost, and training speed of differentially private (DP) deep learning, especially on large vision and language mode…
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
Yuxiang Wang, Xiao Yan, Chuang Hu +5
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features. Contrastive Learn…
Improved Differentially Private Regression via Gradient Boosting
Shuai Tang, Sergul Aydore, Michael Kearns +5
We revisit the problem of differentially private squared error linear regression. We observe that existing state-of-the-art methods are sensitive to the choice of hyperparameters -…
Joint Hacking and Latent Hazard Rate Estimation
Ziqi Liu, Alexander J. Smola, Kyle Soska +2
In this paper we describe an algorithm for predicting the websites at risk in a long range hacking activity, while jointly inferring the provenance and evolution of vulnerabilities…