151 citations · 632 across the 84 of their papers we have counts for
13 papers · 1 filter
Normalizing flow neural networks by JKO scheme
Chen Xu, Xiuyuan Cheng, Yao Xie
Normalizing flow is a class of deep generative models for efficient sampling and likelihood estimation, which achieves attractive performance, particularly in high dimensions. The…
Novelty Detection for Election Fraud: A Case Study with Agent-Based Simulation Data
Khurram Yamin, Nima Jadali, Dima Nazzal +1
In this paper, we propose a robust election simulation model and independently developed election anomaly detection algorithm that demonstrates the simulation's utility. The simula…
Online Detection Of Supply Chain Network Disruptions Using Sequential Change-Point Detection for Hawkes Processes
Khurram Yamin, Haoyun Wang, Benoit Montreuil +1
In this paper, we attempt to detect an inflection or change-point resulting from the Covid-19 pandemic on supply chain data received from a large furniture company. To accomplish t…
Tides Need STEMMED: A Locally Operating Spatio-Temporal Mutually Exciting Point Process with Dynamic Network for Improving Opioid Overdose Death Prediction
Che-Yi Liao, Gian-Gabriel P. Garcia, Kamran Paynabar +3
We develop a Spatio-TEMporal Mutually Exciting point process with Dynamic network (STEMMED), i.e., a point process network wherein each node models a unique community-drug event st…
Data-Adaptive Symmetric CUSUM for Sequential Change Detection
Nauman Ahad, Mark A. Davenport, Yao Xie
Detecting change points sequentially in a streaming setting, especially when both the mean and the variance of the signal can change, is often a challenging task. A key difficulty…
Sequential change-point detection: Computation versus statistical performance
Haoyun Wang, Yao Xie
Change-point detection studies the problem of detecting the changes in the underlying distribution of the data stream as soon as possible after the change happens. Modern large-sca…