An alternative complementarity formulation for primal-dual interior-point methods keeps linear systems spectrally bounded near the solution, enabling stable single-precision solves and differentiation for bilevel and end-to-end learning.
Sparse inverse covariance estimation with the graphical lasso.Biostatistics, 9(3):432–441, 2008
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4representative citing papers
SSTS extracts causal topological orders from score functions via Schur complements of the Score-Jacobian Information Matrix, bypassing acyclicity optimization.
Graphical SLOPE achieves root-n consistent precision matrix estimation with asymptotic edge clustering, and TSLOPE reduces variability under elliptical heavy-tailed distributions compared to Gaussian-loss versions.
q2-classo and q2-gglasso are QIIME 2 plugins that implement sparse log-contrast regression/classification and graphical lasso-based network estimation for microbial compositional data.
citing papers explorer
-
A Differentiable Interior-Point Method in Single Precision
An alternative complementarity formulation for primal-dual interior-point methods keeps linear systems spectrally bounded near the solution, enabling stable single-precision solves and differentiation for bilevel and end-to-end learning.
-
Optimization-Free Topological Sort for Causal Discovery via the Schur Complement of Score Jacobians
SSTS extracts causal topological orders from score functions via Schur complements of the Score-Jacobian Information Matrix, bypassing acyclicity optimization.
-
Asymptotic Theory for Graphical SLOPE: Precision Estimation and Pattern Convergence
Graphical SLOPE achieves root-n consistent precision matrix estimation with asymptotic edge clustering, and TSLOPE reduces variability under elliptical heavy-tailed distributions compared to Gaussian-loss versions.
-
Sparse regression, classification, and microbial network estimation in QIIME2 with q2-classo and q2-gglasso
q2-classo and q2-gglasso are QIIME 2 plugins that implement sparse log-contrast regression/classification and graphical lasso-based network estimation for microbial compositional data.