27 citations · 35 across the 6 of their papers we have counts for
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cs.LG2022
Reconstructing Training Data from Model Gradient, Provably
Zihan Wang, Jason D. Lee, Qi Lei
Understanding when and how much a model gradient leaks information about the training sample is an important question in privacy. In this paper, we present a surprising result: eve…
cs.IR2022★ 27 cited
Debiasing Learning for Membership Inference Attacks Against Recommender Systems
Zihan Wang, Na Huang, Fei Sun +5
Learned recommender systems may inadvertently leak information about their training data, leading to privacy violations. We investigate privacy threats faced by recommender systems…
cs.CL2022
Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking
Jinyu Guo, Kai Shuang, Jijie Li +2
In dialogue state tracking, dialogue history is a crucial material, and its utilization varies between different models. However, no matter how the dialogue history is used, each e…