Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Xi Luo

Xi Luo

AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph


May 28, 2020
Xin Chen, Yuwei Li, Xi Luo, Tianjia Shao, Jingyi Yu, Kun Zhou, Youyi Zheng

Figure 1 for AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph
Figure 2 for AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph
Figure 3 for AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph
Figure 4 for AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph
* IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 3, pp. 1466-1475, 1 March 2020 
 * 10 pages, 12 figures 
  Access Paper or Ask Questions

Towards 3D Human Shape Recovery Under Clothing


Apr 09, 2019
Xin Chen, Anqi Pang, Yu Zhu, Yuwei Li, Xi Luo, Ge Zhang, Peihao Wang, Yingliang Zhang, Shiying Li, Jingyi Yu

Figure 1 for Towards 3D Human Shape Recovery Under Clothing
Figure 2 for Towards 3D Human Shape Recovery Under Clothing
Figure 3 for Towards 3D Human Shape Recovery Under Clothing
Figure 4 for Towards 3D Human Shape Recovery Under Clothing
* 10 pages, 8 figures 
  Access Paper or Ask Questions

Model Assisted Variable Clustering: Minimax-optimal Recovery and Algorithms


Apr 16, 2018
Florentina Bunea, Christophe Giraud, Xi Luo, Martin Royer, Nicolas Verzelen

Figure 1 for Model Assisted Variable Clustering: Minimax-optimal Recovery and Algorithms
Figure 2 for Model Assisted Variable Clustering: Minimax-optimal Recovery and Algorithms
Figure 3 for Model Assisted Variable Clustering: Minimax-optimal Recovery and Algorithms
Figure 4 for Model Assisted Variable Clustering: Minimax-optimal Recovery and Algorithms
* Maintext: 40 pages; supplementary information: 21 pages 
  Access Paper or Ask Questions

Granger Mediation Analysis of Multiple Time Series with an Application to fMRI


Sep 15, 2017
Yi Zhao, Xi Luo

Figure 1 for Granger Mediation Analysis of Multiple Time Series with an Application to fMRI
Figure 2 for Granger Mediation Analysis of Multiple Time Series with an Application to fMRI
Figure 3 for Granger Mediation Analysis of Multiple Time Series with an Application to fMRI
Figure 4 for Granger Mediation Analysis of Multiple Time Series with an Application to fMRI
* 59 pages. Presented at the 2017 ENAR, JSM, and other meetings 
  Access Paper or Ask Questions

Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions


Dec 22, 2016
Weidong Liu, Xi Luo

Figure 1 for Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions
Figure 2 for Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions
Figure 3 for Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions
Figure 4 for Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions
* Journal of Multivariate Analysis. 2015; 135:153 -62 
 * Maintext: 24 pages. Supplement: 13 pages. R package scio implementing the proposed method is available on CRAN at https://cran.r-project.org/package=scio . Published in J of Multivariate Analysis at http://www.sciencedirect.com/science/article/pii/S0047259X14002607 
  Access Paper or Ask Questions

Pathway Lasso: Estimate and Select Sparse Mediation Pathways with High Dimensional Mediators


Mar 24, 2016
Yi Zhao, Xi Luo

Figure 1 for Pathway Lasso: Estimate and Select Sparse Mediation Pathways with High Dimensional Mediators
Figure 2 for Pathway Lasso: Estimate and Select Sparse Mediation Pathways with High Dimensional Mediators
Figure 3 for Pathway Lasso: Estimate and Select Sparse Mediation Pathways with High Dimensional Mediators
Figure 4 for Pathway Lasso: Estimate and Select Sparse Mediation Pathways with High Dimensional Mediators
* 26 pages and 7 figures. Presented at the 2016 ENAR meeting, March 8, 2016, see slides at https://rluo.github.io/slides/MultipleMediator_ENAR_2016.html 
  Access Paper or Ask Questions

A Hierarchical Graphical Model for Big Inverse Covariance Estimation with an Application to fMRI


Apr 07, 2014
Xi Luo

Figure 1 for A Hierarchical Graphical Model for Big Inverse Covariance Estimation with an Application to fMRI
Figure 2 for A Hierarchical Graphical Model for Big Inverse Covariance Estimation with an Application to fMRI
Figure 3 for A Hierarchical Graphical Model for Big Inverse Covariance Estimation with an Application to fMRI
Figure 4 for A Hierarchical Graphical Model for Big Inverse Covariance Estimation with an Application to fMRI
* An R package of the proposed method will be publicly available on CRAN. This paper has been presented orally at Yale University on Feburary 18, 2014, and at the Eastern North American Region Meeting of the International Biometric Society on March 18, 2014 
  Access Paper or Ask Questions