13 citations · 63 across the 14 of their papers we have counts for
6 papers · 1 filter
Scalable Membership Inference Attacks via Quantile Regression
Martin Bertran, Shuai Tang, Michael Kearns +3
Membership inference attacks are designed to determine, using black box access to trained models, whether a particular example was used in training or not. Membership inference can…
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 -…
Private Synthetic Data for Multitask Learning and Marginal Queries
Giuseppe Vietri, Cedric Archambeau, Sergul Aydore +6
We provide a differentially private algorithm for producing synthetic data simultaneously useful for multiple tasks: marginal queries and multitask machine learning (ML). A key inn…
Deep Transfer Learning with Ridge Regression
Shuai Tang, Virginia R. de Sa
The large amount of online data and vast array of computing resources enable current researchers in both industry and academia to employ the power of deep learning with neural netw…
Similarity of Neural Networks with Gradients
Shuai Tang, Wesley J. Maddox, Charlie Dickens +2
A suitable similarity index for comparing learnt neural networks plays an important role in understanding the behaviour of the highly-nonlinear functions, and can provide insights…
An Empirical Study on Post-processing Methods for Word Embeddings
Shuai Tang, Mahta Mousavi, Virginia R. de Sa
Word embeddings learnt from large corpora have been adopted in various applications in natural language processing and served as the general input representations to learning syste…