OgBench benchmarks GNNs on omics graphs in the n << p regime and reports that standard GNNs often fail to beat MLPs and classical baselines.
Regression shrinkage and selection via the lasso.Journal of the Royal Statistical Society: Series B, 58(1):267–288, 1996
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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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OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data
OgBench benchmarks GNNs on omics graphs in the n << p regime and reports that standard GNNs often fail to beat MLPs and classical baselines.
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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.