User Tools

Site Tools


matrix_and_statistical_ai_reading_group

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
matrix_and_statistical_ai_reading_group [2010/03/11 12:16]
128.83.158.97
matrix_and_statistical_ai_reading_group [2013/04/23 21:55] (current)
127.0.0.1 converted to 1.6 markup
Line 2: Line 2:
  
 Mailing list: matrix-sai at utlists.utexas Mailing list: matrix-sai at utlists.utexas
 +
 +=== III ===
 +|Date | 31 Mar 2010. |
 +|Paper | Pradeep Ravikumar et al: High-dimensional covariance estimation by minimizing l_1-penalized log-determinant divergence http://​www.stat.berkeley.edu/​tech-reports/​767.pdf| |
 +|Presenter | Matyas Sustik | |
 +|Notes | This paper contains a good introduction to covariance matrix estimation; the "large p small n case"; the connection to logdet divergence before delving into its main results. | |
  
 === II. Intro to the use of Support graphs in finding preconditioners === === II. Intro to the use of Support graphs in finding preconditioners ===
Line 22: Line 28:
  
 ===== Papers proposed ===== ===== Papers proposed =====
-|Pradeep Ravikumar et al: High-dimensional covariance estimation by minimizing l_1-penalized log-determinant divergence http://​www.stat.berkeley.edu/​tech-reports/​767.pdf| | 
-|Matyas Sustik | | 
-|This paper contains a good introduction to covariance matrix estimation; the "large p small n case"; the connection to logdet divergence before delving into its main results. | | 
  
matrix_and_statistical_ai_reading_group.1268331413.txt.gz ยท Last modified: 2010/03/11 12:16 by 128.83.158.97