249 citations · 507 across the 22 of their papers we have counts for
13 papers · 1 filter
Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations
Mimee Xu, Jiankai Sun, Xin Yang +2
People break up, miscarry, and lose loved ones. Their online streaming and shopping recommendations, however, do not necessarily update, and may serve as unhappy reminders of their…
Efficient Attention via Control Variates
Lin Zheng, Jianbo Yuan, Chong Wang +1
Random-feature-based attention (RFA) is an efficient approximation of softmax attention with linear runtime and space complexity. However, the approximation gap between RFA and con…
Collaborative Anomaly Detection
Ke Bai, Aonan Zhang, Zhizhong Li +3
In recommendation systems, items are likely to be exposed to various users and we would like to learn about the familiarity of a new user with an existing item. This can be formula…
DPAUC: Differentially Private AUC Computation in Federated Learning
Jiankai Sun, Xin Yang, Yuanshun Yao +3
Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants. The prior work on…
Differentially Private AUC Computation in Vertical Federated Learning
Jiankai Sun, Xin Yang, Yuanshun Yao +3
Federated learning has gained great attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple parties. As a sub-category, vertical federa…
Linear Complexity Randomized Self-attention Mechanism
Lin Zheng, Chong Wang, Lingpeng Kong
Recently, random feature attentions (RFAs) are proposed to approximate the softmax attention in linear time and space complexity by linearizing the exponential kernel. In this pape…