19 citations · 39 across the 16 of their papers we have counts for
16 papers
Quickest Change Detection in Parametric Models With 1-Bit Measurements
Liyan Xie, Martina Cardone
We consider the quickest change detection from 1-bit quantized observations, where the post-change distribution is a parametric model with unknown parameters and the quantization t…
Distributionally Robust Quickest Change Detection using Wasserstein Uncertainty Sets
Liyan Xie, Yuchen Liang, Venugopal V. Veeravalli
The problem of quickest detection of a change in the distribution of a sequence of independent observations is considered. It is assumed that the pre-change distribution is known (…
MissDiff: Training Diffusion Models on Tabular Data with Missing Values
Yidong Ouyang, Liyan Xie, Chongxuan Li +1
The diffusion model has shown remarkable performance in modeling data distributions and synthesizing data. However, the vanilla diffusion model requires complete or fully observed…
Neural Differential Recurrent Neural Network with Adaptive Time Steps
Yixuan Tan, Liyan Xie, Xiuyuan Cheng
The neural Ordinary Differential Equation (ODE) model has shown success in learning complex continuous-time processes from observations on discrete time stamps. In this work, we co…
Window-Limited CUSUM for Sequential Change Detection
Liyan Xie, George V. Moustakides, Yao Xie
We study the parametric online changepoint detection problem, where the underlying distribution of the streaming data changes from a known distribution to an alternative that is of…
Minimax Robust Quickest Change Detection using Wasserstein Ambiguity Sets
Liyan Xie
We study the robust quickest change detection under unknown pre- and post-change distributions. To deal with uncertainties in the data-generating distributions, we formulate two da…
PERCEPT: a new online change-point detection method using topological data analysis
Xiaojun Zheng, Simon Mak, Liyan Xie +1
Topological data analysis (TDA) provides a set of data analysis tools for extracting embedded topological structures from complex high-dimensional datasets. In recent years, TDA ha…
Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data
Shixiang Zhu, Alexander Bukharin, Liyan Xie +4
Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incid…
Robust Hypothesis Testing with Wasserstein Uncertainty Sets
Liyan Xie, Rui Gao, Yao Xie
We consider a data-driven robust hypothesis test where the optimal test will minimize the worst-case performance regarding distributions that are close to the empirical distributio…
Sequential (Quickest) Change Detection: Classical Results and New Directions
Liyan Xie, Shaofeng Zou, Yao Xie +1
Online detection of changes in stochastic systems, referred to as sequential change detection or quickest change detection, is an important research topic in statistics, signal pro…
Optimality of Graph Scanning Statistic for Online Community Detection
Liyan Xie, Yao Xie
Sequential change-point detection for graphs is a fundamental problem for streaming network data types and has wide applications in social networks and power systems. Given fixed v…
Sequential change-point detection for mutually exciting point processes over networks
Haoyun Wang, Liyan Xie, Yao Xie +2
We present a new CUSUM procedure for sequentially detecting change-point in the self and mutual exciting processes, a.k.a. Hawkes networks using discrete events data. Hawkes networ…
Sequential Change Detection by Optimal Weighted Divergence
Liyan Xie, Yao Xie
We present a new non-parametric statistic, called the weighed divergence, based on empirical distributions for sequential change detection. We start by constructing the we…
Distributionally Robust Weighted -Nearest Neighbors
Shixiang Zhu, Liyan Xie, Minghe Zhang +2
Learning a robust classifier from a few samples remains a key challenge in machine learning. A major thrust of research has been focused on developing -nearest neighbor (-NN)…
Spectral CUSUM for Online Network Structure Change Detection
Minghe Zhang, Liyan Xie, Yao Xie
Detecting abrupt changes in the community structure of a network from noisy observations is a fundamental problem in statistics and machine learning. This paper presents an online…
Sequential Subspace Change-Point Detection
Liyan Xie, Yao Xie, George V. Moustakides
We consider the online monitoring of multivariate streaming data for changes that are characterized by an unknown subspace structure manifested in the covariance matrix. In particu…