34 citations · 79 across the 12 of their papers we have counts for
13 papers
: 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…
Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge
Jingge Wang, Liyan Xie, Yao Xie +2
Domain generalization aims at learning a universal model that performs well on unseen target domains, incorporating knowledge from multiple source domains. In this research, we con…
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…
Survival Analysis with Graph-Based Regularization for Predictors
Liyan Xie, Xi He, Pinar Keskinocak +1
We study the variable selection problem in survival analysis to identify the most important factors affecting survival time. Our method incorporates prior knowledge of mutual corre…
High-resolution Spatio-temporal Model for County-level COVID-19 Activity in the U.S
Shixiang Zhu, Alexander Bukharin, Liyan Xie +3
We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases one-week ahead of the current time, at the county-level…
Uncertainty Quantification for Inferring Hawkes Networks
Haoyun Wang, Liyan Xie, Alex Cuozzo +2
Multivariate Hawkes processes are commonly used to model streaming networked event data in a wide variety of applications. However, it remains a challenge to extract reliable infer…
Convex Parameter Recovery for Interacting Marked Processes
Anatoli Juditsky, Arkadi Nemirovski, Liyan Xie +1
We introduce a new general modeling approach for multivariate discrete event data with categorical interacting marks, which we refer to as marked Bernoulli processes. In the propos…
Asynchronous Multi-Sensor Change-Point Detection for Seismic Tremors
Liyan Xie, Yao Xie, George V. Moustakides
We consider the sequential change-point detection for asynchronous multi-sensors, where each sensor observe a signal (due to change-point) at different times. We propose an asynchr…
First-order optimal sequential subspace change-point detection
Liyan Xie, George V. Moustakides, Yao Xie
We consider the sequential change-point detection problem of detecting changes that are characterized by a subspace structure. Such changes are frequent in high-dimensional streami…
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
Rui Gao, Liyan Xie, Yao Xie +1
We develop a novel computationally efficient and general framework for robust hypothesis testing. The new framework features a new way to construct uncertainty sets under the null…
Sequential detection of low-rank changes using extreme eigenvalues
Liyan Xie, Yao Xie
We study the problem of detecting an abrupt change to the signal covariance matrix. In particular, the covariance changes from a "white" identity matrix to an unknown spiked or low…
Nearly second-order asymptotic optimality of sequential change-point detection with one-sample updates
Yang Cao, Liyan Xie, Yao Xie +1
Sequential change-point detection when the distribution parameters are unknown is a fundamental problem in statistics and machine learning. When the post-change parameters are unkn…
Robust Sequential Change-Point Detection by Convex Optimization
Yang Cao, Yao Xie
We address the computational challenge of finding the robust sequential change-point detection procedures when the pre- and post-change distributions are not completely specified.…