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
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
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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.
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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.
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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.