no code implementations • 13 Feb 2023 • Jun Tao, Qian Chen, James W. Snyder Jr., Arava Sai Kumar, Amirhossein Meisami, Lingzhou Xue
Marketers employ various online advertising channels to reach customers, and they are particularly interested in attribution for measuring the degree to which individual touchpoints contribute to an eventual conversion.
no code implementations • 29 Dec 2022 • Teng Zhang, Haoyi Yang, Lingzhou Xue
Sparse principal component analysis (SPCA) has been widely used for dimensionality reduction and feature extraction in high-dimensional data analysis.
no code implementations • 11 Jul 2022 • Qi Zhang, Bing Li, Lingzhou Xue
We introduce a novel framework for nonlinear sufficient dimension reduction where both the predictor and the response are distributional data, which are modeled as members of a metric space.
no code implementations • 29 Dec 2021 • Jun Tao, Bing Li, Lingzhou Xue
We introduce a nonparametric graphical model for discrete node variables based on additive conditional independence.
no code implementations • 1 Oct 2021 • Qi Zhang, Lingzhou Xue, Bing Li
In this paper, we introduce a flexible sufficient dimension reduction (SDR) method for Fr\'echet regression to achieve two purposes: to mitigate the curse of dimensionality caused by high-dimensional predictors and to provide a visual inspection tool for Fr\'echet regression.
no code implementations • 30 Sep 2021 • Bingyuan Liu, Qi Zhang, Lingzhou Xue, Peter X. K. Song, Jian Kang
It is of importance to develop statistical techniques to analyze high-dimensional data in the presence of both complex dependence and possible outliers in real-world applications such as imaging data analyses.
no code implementations • 28 Jan 2021 • Bingyuan Liu, Christopher Malon, Lingzhou Xue, Erik Kruus
Finally, we empirically show that our designed network architecture is more robust against state-of-art gradient descent based attacks, such as a PGD attack on the benchmark datasets MNIST and CIFAR10.
no code implementations • 1 Jan 2021 • Bingyuan Liu, Yogesh Balaji, Lingzhou Xue, Martin Renqiang Min
Attention mechanisms have advanced state-of-the-art deep learning models in many machine learning tasks.
no code implementations • 18 Jul 2020 • Zhongruo Wang, Bingyuan Liu, Shixiang Chen, Shiqian Ma, Lingzhou Xue, Hongyu Zhao
This paper considers a widely adopted model for SSC, which can be formulated as an optimization problem over the Stiefel manifold with nonsmooth and nonconvex objective.
no code implementations • 31 May 2020 • Xiufan Yu, Danning Li, Lingzhou Xue
Testing large covariance matrices is of fundamental importance in statistical analysis with high-dimensional data.
no code implementations • 3 May 2020 • Bokun Wang, Shiqian Ma, Lingzhou Xue
However, most of the existing Riemannian stochastic algorithms require the objective function to be differentiable, and they do not apply to the case where the objective function is nonsmooth.
no code implementations • 25 Sep 2019 • Bingyuan Liu, Yogesh Balaji, Lingzhou Xue, Martin Renqiang Min
Attention mechanisms have advanced the state of the art in several machine learning tasks.
no code implementations • 27 Mar 2019 • Shixiang Chen, Shiqian Ma, Lingzhou Xue, Hui Zou
Sparse principal component analysis (PCA) and sparse canonical correlation analysis (CCA) are two essential techniques from high-dimensional statistics and machine learning for analyzing large-scale data.
no code implementations • 21 Dec 2017 • Amal Agarwal, Lingzhou Xue
The power of our proposed methods is demonstrated in simulation studies and a real application to sulfate pollution network analysis in Ohio watershed located in Pennsylvania, United States.
no code implementations • 20 Dec 2017 • Kevin H. Lee, Lingzhou Xue, David R. Hunter
To choose the number of communities, we use conditional likelihood to construct an effective model selection criterion.
no code implementations • 1 May 2017 • Wei Luo, Lingzhou Xue, Jiawei Yao, Xiufan Yu
Assuming that the predictors affect the response through the latent factors, we propose to first conduct factor analysis and then apply sufficient dimension reduction on the estimated factors, to derive the reduced data for subsequent forecasting.
no code implementations • 31 Dec 2015 • Kevin Lee, Lingzhou Xue
Graphical model has been widely used to investigate the complex dependence structure of high-dimensional data, and it is common to assume that observed data follow a homogeneous graphical model.
no code implementations • 30 Dec 2015 • Danning Li, Lingzhou Xue
Using extreme-value form statistics to test against sparse alternatives and using quadratic form statistics to test against dense alternatives are two important testing procedures for high-dimensional independence.
no code implementations • 27 May 2015 • Jianqing Fan, Lingzhou Xue, Jiawei Yao
Our method and theory allow the number of predictors to be larger than the number of observations.
no code implementations • 22 Oct 2012 • Jianqing Fan, Lingzhou Xue, Hui Zou
Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation.