no code implementations • 30 Oct 2023 • Xiaojun Zheng, Simon Mak, Liyan Xie, Yao Xie
Robust Principal Component Analysis (RPCA) is a widely used method for recovering low-rank structure from data matrices corrupted by significant and sparse outliers.
no code implementations • 2 Jul 2023 • Yidong Ouyang, Liyan Xie, Chongxuan Li, Guang Cheng
The diffusion model has shown remarkable performance in modeling data distributions and synthesizing data.
1 code implementation • 2 Jun 2023 • Yixuan Tan, Liyan Xie, Xiuyuan Cheng
We propose an RNN-based model, called RNN-ODE-Adap, that uses a neural ODE to represent the time development of the hidden states, and we adaptively select time steps based on the steepness of changes of the data over time so as to train the model more efficiently for the "spike-like" time series.
no code implementations • 18 Oct 2022 • Yidong Ouyang, Liyan Xie, Guang Cheng
Among various deep generative models, the diffusion model has been shown to produce high-quality synthetic images and has achieved good performance in improving the adversarial robustness.
no code implementations • 11 Jul 2022 • Jingge Wang, Liyan Xie, Yao Xie, Shao-Lun Huang, Yang Li
Domain generalization aims at learning a universal model that performs well on unseen target domains, incorporating knowledge from multiple source domains.
no code implementations • 8 Mar 2022 • Xiaojun Zheng, Simon Mak, Liyan Xie, Yao Xie
This yields a non-parametric, topology-aware framework which can efficiently detect online changes from high-dimensional data streams.
no code implementations • 8 Sep 2021 • Jingge Wang, Yang Li, Liyan Xie, Yao Xie
Given multiple source domains, domain generalization aims at learning a universal model that performs well on any unseen but related target domain.
no code implementations • 31 May 2021 • Shixiang Zhu, Alexander Bukharin, Liyan Xie, Khurram Yamin, Shihao Yang, Pinar Keskinocak, Yao Xie
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) incidence (hotspots) and offers support to local health departments to limit the spread of the disease.
no code implementations • 11 Feb 2021 • 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.
Change Point Detection Online Community Detection Statistics Theory Statistics Theory
no code implementations • 10 Feb 2021 • Haoyun Wang, Liyan Xie, Yao Xie, Alex Cuozzo, Simon Mak
We present a new CUSUM procedure for sequentially detecting change-point in the self and mutual exciting processes, a. k. a.
no code implementations • NeurIPS 2020 • Haoyun Wang, Liyan Xie, Alex Cuozzo, Simon Mak, Yao Xie
Multivariate Hawkes processes are commonly used to model streaming networked event data in a wide variety of applications.
no code implementations • 7 Jun 2020 • Shixiang Zhu, Liyan Xie, Minghe Zhang, Rui Gao, Yao Xie
When the samples are limited, robustness is especially crucial to ensure the generalization capability of the classifier.
no code implementations • 29 Mar 2020 • Anatoli Juditsky, Arkadi Nemirovski, Liyan Xie, Yao Xie
In the proposed model, the probability of an event of a specific category to occur in a location may be influenced by past events at this and other locations.
no code implementations • 20 Oct 2019 • 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.
no code implementations • NeurIPS 2018 • Rui Gao, Liyan Xie, Yao Xie, Huan Xu
We develop a novel computationally efficient and general framework for robust hypothesis testing.
no code implementations • 15 Jun 2017 • Liyan Xie, Yao Xie
We study the problem of detecting an abrupt change to the signal covariance matrix.
no code implementations • 19 May 2017 • Yang Cao, Liyan Xie, Yao Xie, Huan Xu
Our proof is achieved by making a connection between sequential change-point and online convex optimization and leveraging the logarithmic regret bound property of online mirror descent algorithm.