Proposes PcovRnnp method enabling simultaneous dimension reduction and regularized coefficient estimation via nuclear norm penalty in high-dimensional settings.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Most neural architectures admit a GSVD representation making the nonlinear portion left-invertible and norm-preserving before the final linear layer.
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Principal Covariate Regression with Nuclear Norm Penalty
Proposes PcovRnnp method enabling simultaneous dimension reduction and regularized coefficient estimation via nuclear norm penalty in high-dimensional settings.
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A Generalized Singular Value Theory for Neural Networks
Most neural architectures admit a GSVD representation making the nonlinear portion left-invertible and norm-preserving before the final linear layer.