HIGH-DIMENSIONAL COVARIANCE ESTIMATION BY MINIMIZING l1-PENALIZED LOG-DETERMINANT DIVERGENCE BY PRADEEP RAVIKUMAR

By Pradeep Ravikumar, Martin J. Wainwright, Yu. 2009

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Tommaso Cai
2. Dino Sejdinovic
3. Harrison H. Zhou
4. Arthur Gretton
5. Krikamol Muandet
6. Wicher P. Bergsma
7. Mathias Drton
8. Bernhard Schölkopf

Citations in the corpus

• A Kernel Test for Three-Variable Interactions (2013, methodology): “If the graphical model is assumed to be Gaussian, then second order interaction statistics may be used to construct an undirected graph [11, 12].”
• Graph Learning From Data Under Laplacian and Structural Constraints (2016, methodology): “75(smax √ log(n)/k) | r = 1, 2, 3, · · · , 14}, (62) where smax =maxi 6=j |(S)ij | is the maximum off-diagonal entry of S in absolute value, and the scaling term √ log(n)/k is used for adjusting the regularization according to k and n as suggested in [58] [59].”
• Estimating Sparse Precision Matrix: Optimal Rates of Convergence and Adaptive Estimation (2012, background): “See also Friedman, Hastie and Tibshirani (2008), d’Aspremont, Banerjee and El Ghaoui (2008), Rothman et al. (2008), Lam and Fan (2009), and Ravikumar et al. (2011).”