8 citations · 20 across the 11 of their papers we have counts for
11 papers
Adaptive Testing for High-dimensional Data
Yangfan Zhang, Runmin Wang, Xiaofeng Shao
In this article, we propose a class of -norm based U-statistics for a family of global testing problems related to high-dimensional data. This includes testing of mean vector…
Dimension-agnostic Change Point Detection
Hanjia Gao, Runmin Wang, Xiaofeng Shao
Change point testing for high-dimensional data has attracted a lot of attention in statistics and machine learning owing to the emergence of high-dimensional data with structural b…
Robust Inference for Change Points in High Dimension
Feiyu Jiang, Runmin Wang, Xiaofeng Shao
This paper proposes a new test for a change point in the mean of high-dimensional data based on the spatial sign and self-normalization. The test is easy to implement with no tunin…
Treating Crowdsourcing as Examination: How to Score Tasks and Online Workers?
Guangyang Han, Sufang Li, Runmin Wang +1
Crowdsourcing is an online outsourcing mode which can solve the current machine learning algorithm's urge need for massive labeled data. Requester posts tasks on crowdsourcing plat…
Meta Cross-Modal Hashing on Long-Tailed Data
Runmin Wang, Guoxian Yu, Carlotta Domeniconi +1
Due to the advantage of reducing storage while speeding up query time on big heterogeneous data, cross-modal hashing has been extensively studied for approximate nearest neighbor s…
Cross-modal Zero-shot Hashing by Label Attributes Embedding
Runmin Wang, Guoxian Yu, Lei Liu +3
Cross-modal hashing (CMH) is one of the most promising methods in cross-modal approximate nearest neighbor search. Most CMH solutions ideally assume the labels of training and test…