14 citations · 16 across the 3 of their papers we have counts for
3 papers
cs.CR2022
Data Provenance via Differential Auditing
Xin Mu, Ming Pang, Feida Zhu
Auditing Data Provenance (ADP), i.e., auditing if a certain piece of data has been used to train a machine learning model, is an important problem in data provenance. The feasibili…
cs.SI2019★ 2 cited
Unsupervised User Identity Linkage via Factoid Embedding
Wei Xie, Xin Mu, Roy Ka-Wei Lee +2
User identity linkage (UIL), the problem of matching user account across multiple online social networks (OSNs), is widely studied and important to many real-world applications. Mo…
cs.LG2016★ 14 cited
Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees
Xin Mu, Kai Ming Ting, Zhi-Hua Zhou
This paper investigates an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC. The common approach is to treat it as a classifica…