9 citations · 19 across the 7 of their papers we have counts for
4 papers · 1 filter
Epsilon*: Privacy Metric for Machine Learning Models
Diana M. Negoescu, Humberto Gonzalez, Saad Eddin Al Orjany +9
We introduce Epsilon*, a new privacy metric for measuring the privacy risk of a single model instance prior to, during, or after deployment of privacy mitigation strategies. The me…
Cross-domain error minimization for unsupervised domain adaptation
Yuntao Du, Yinghao Chen, Fengli Cui +2
Unsupervised domain adaptation aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Previous methods focus on learning domain-invariant features t…
Learning transferable and discriminative features for unsupervised domain adaptation
Yuntao Du, Ruiting Zhang, Xiaowen Zhang +3
Although achieving remarkable progress, it is very difficult to induce a supervised classifier without any labeled data. Unsupervised domain adaptation is able to overcome this cha…
Dual Adversarial Domain Adaptation
Yuntao Du, Zhiwen Tan, Qian Chen +3
Unsupervised domain adaptation aims at transferring knowledge from the labeled source domain to the unlabeled target domain. Previous adversarial domain adaptation methods mostly a…