6 papers
Graph-Based Change-Point Detection for Partially Observed High-Dimensional Data
Mingshuo Liu, Hao Chen
Partial missingness is common in high-dimensional data, but most existing change-point procedures are developed for fully observed sequences. We introduce gMiss, a graph-based fram…
A Trainable Centrality Framework for Modern Data
Minh Duc Vu, Mingshuo Liu, Doudou Zhou
Measuring how central or typical a data point is underpins robust estimation, ranking, and outlier detection, but classical depth notions become expensive and unstable in high dime…
WISE: A Weighted Similarity Aggregation Test for Serial Independence
Qihua Zhu, Mingshuo Liu, Yuefeng Han +1
We propose a nonparametric test for serial independence that aggregates pairwise similarities of observations with lag-dependent weights. The resulting statistic is powerful to gen…
Single Index Bandits: Generalized Linear Contextual Bandits with Unknown Reward Functions
Yue Kang, Mingshuo Liu, Bongsoo Yi +4
Generalized linear bandits have been extensively studied due to their broad applicability in real-world online decision-making problems. However, these methods typically assume tha…
Robust Max Statistics for High-Dimensional Inference
Mingshuo Liu, Miles E. Lopes
Although much progress has been made in the theory and application of bootstrap approximations for max statistics in high dimensions, the literature has largely been restricted to…
Generalized Independence Test
Mingshuo Liu, Doudou Zhou, Hao Chen
Testing independence is a widely encountered problem in modern data analysis, especially in high-dimensional settings where complex dependency structures are common. Traditional me…