13 citations · 49 across the 8 of their papers we have counts for
5 papers · 1 filter
A Central Limit Theorem for Differentially Private Query Answering
Jinshuo Dong, Weijie J. Su, Linjun Zhang
Perhaps the single most important use case for differential privacy is to privately answer numerical queries, which is usually achieved by adding noise to the answer vector. The ce…
Federated -Differential Privacy
Qinqing Zheng, Shuxiao Chen, Qi Long +1
Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their lo…
Benign Overfitting and Noisy Features
Zhu Li, Weijie Su, Dino Sejdinovic
Modern machine learning often operates in the regime where the number of parameters is much higher than the number of data points, with zero training loss and yet good generalizati…
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion
Qinqing Zheng, Jinshuo Dong, Qi Long +1
Datasets containing sensitive information are often sequentially analyzed by many algorithms. This raises a fundamental question in differential privacy regarding how the overall p…
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
Zhiqi Bu, Jason Klusowski, Cynthia Rush +1
SLOPE is a relatively new convex optimization procedure for high-dimensional linear regression via the sorted l1 penalty: the larger the rank of the fitted coefficient, the larger…