Publications (13)
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…
Adaptive Prototype Replay for Class Incremental Semantic Segmentation
Guilin Zhu, Dongyue Wu, Changxin Gao +3
Class incremental semantic segmentation (CISS) aims to segment new classes during continual steps while preventing the forgetting of old knowledge. Existing methods alleviate catas…
High-dimensional Change-point Detection Using Generalized Homogeneity Metrics
Shubhadeep Chakraborty, Xianyang Zhang, Runmin Wang
Change-point detection is a classical problem in statistics. We address the problem of detecting abrupt changes in the data-generating distributions of a sequence of high-dimension…
Inference for Change Points in High Dimensional Data via Self-Normalization
Runmin Wang, Changbo Zhu, Stanislav Volgushev +1
This article considers change point testing and estimation for a sequence of high-dimensional data. In the case of testing for a mean shift for high-dimensional independent data, w…
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…
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…
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…
Adaptive Inference for Change Points in High-Dimensional Data
Yangfan Zhang, Runmin Wang, Xiaofeng Shao
In this article, we propose a class of test statistics for a change point in the mean of high-dimensional independent data. Our test integrates the U-statistic based approach in a…
Adaptive Change Point Monitoring for High-Dimensional Data
Teng Wu, Runmin Wang, Hao Yan +1
In this paper, we propose a class of monitoring statistics for a mean shift in a sequence of high-dimensional observations. Inspired by the recent U-statistic based retrospective t…
Dating the Break in High-dimensional Data
Runmin Wang, Xiaofeng Shao
This paper is concerned with estimation and inference for the location of a change point in the mean of independent high-dimensional data. Our change point location estimator maxim…
Slicing-free Inverse Regression in High-dimensional Sufficient Dimension Reduction
Qing Mai, Xiaofeng Shao, Runmin Wang +1
Sliced inverse regression (SIR, Li 1991) is a pioneering work and the most recognized method in sufficient dimension reduction. While promising progress has been made in theory and…
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…