8 papers
Change-Point Detection via Piecewise Linear Fitting Using MIP
Apoorva Narula, Santanu S. Dey, Yao Xie
We present a new mixed-integer programming (MIP) approach for offline multiple change-point detection by casting the problem as a globally optimal piecewise linear (PWL) fitting pr…
Multi-rank Subspace Change-point Detection with Application in Monitoring Robotic Swarms
Jonghyeok Lee, Yao Xie, Youngser Park +3
We study real-time detection of low-rank changes in the covariance structure of high-dimensional streaming data, motivated by robotic swarm monitoring. Building on the spiked covar…
Neural Spatiotemporal Point Processes: Trends and Challenges
Sumantrak Mukherjee, Mouad Elhamdi, George Mohler +4
Spatiotemporal point processes (STPPs) are probabilistic models for events occurring in continuous space and time. Real-world event data often exhibit intricate dependencies and he…
Point processes with event time uncertainty
Xiuyuan Cheng, Tingnan Gong, Yao Xie
Point processes are widely used statistical models for continuous-time discrete event data, such as medical records, crime reports, and social network interactions, to capture the…
Higher-criticism for sparse multi-stream change-point detection
Tingnan Gong, Alon Kipnis, Yao Xie
We study a statistical procedure based on higher criticism (HC) to address the sparse multi-stream quickest change-point detection problem. Namely, we aim to detect a potential cha…
Nonlinear time-series embedding by monotone variational inequality
Jonathan Y. Zhou, Yao Xie
In the wild, we often encounter collections of sequential data such as electrocardiograms, motion capture, genomes, and natural language, and sequences may be multichannel or symbo…
Distribution-Free Online Change Detection for Low-Rank Images
Tingnan Gong, Seong-Hee Kim, Yao Xie
We present a distribution-free CUSUM procedure designed for online change detection in a time series of low-rank images, particularly when the change causes a mean shift. We repres…
: Robust Principal Component Analysis for Exponential Family Distributions
Xiaojun Zheng, Simon Mak, Liyan Xie +1
Robust Principal Component Analysis (RPCA) is a widely used method for recovering low-rank structure from data matrices corrupted by significant and sparse outliers. These corrupti…