151 citations · 632 across the 84 of their papers we have counts for
16 papers · 1 filter
Causal Structural Learning from Time Series: A Convex Optimization Approach
Song Wei, Yao Xie
Structural learning, which aims to learn directed acyclic graphs (DAGs) from observational data, is foundational to causal reasoning and scientific discovery. Recent advancements f…
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…
Learning Sinkhorn divergences for supervised change point detection
Nauman Ahad, Eva L. Dyer, Keith B. Hengen +2
Many modern applications require detecting change points in complex sequential data. Most existing methods for change point detection are unsupervised and, as a consequence, lack a…
Class-conditioned Domain Generalization via Wasserstein Distributional Robust Optimization
Jingge Wang, Yang Li, Liyan Xie +1
Given multiple source domains, domain generalization aims at learning a universal model that performs well on any unseen but related target domain. In this work, we focus on the do…
Neural Spectral Marked Point Processes
Shixiang Zhu, Haoyun Wang, Zheng Dong +2
Self- and mutually-exciting point processes are popular models in machine learning and statistics for dependent discrete event data. To date, most existing models assume stationary…