Establishes asymptotic consistency of factor estimates and √T-normality in factor-augmented regressions for fixed R ≥ r using anisotropic local laws from random matrix theory.
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4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
For binary classification in the NTK regime, LoRA rank r=1 suffices and is often optimal under cross-entropy loss, reducing the prior sufficient condition from r>=12.
A unified framework for exponential tilting in diffusion and flow models that includes bias-variance decompositions showing finite gradient variance for some methods, norm bounds on adjoint ODEs, and adapted losses with new Crooks and Jarzynski identities.
A Lasso-based screening step followed by low-dimensional mean-variance optimization on the selected assets improves high-dimensional portfolio construction, with a defactoring extension for strong factors.
citing papers explorer
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Fixed-order PCA: Theory for Overestimated Factor Models
Establishes asymptotic consistency of factor estimates and √T-normality in factor-augmented regressions for fixed R ≥ r using anisotropic local laws from random matrix theory.
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Rethinking the Rank Threshold for LoRA Fine-Tuning
For binary classification in the NTK regime, LoRA rank r=1 suffices and is often optimal under cross-entropy loss, reducing the prior sufficient condition from r>=12.
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A unified perspective on fine-tuning and sampling with diffusion and flow models
A unified framework for exponential tilting in diffusion and flow models that includes bias-variance decompositions showing finite gradient variance for some methods, norm bounds on adjoint ODEs, and adapted losses with new Crooks and Jarzynski identities.
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Post-Screening Portfolio Selection
A Lasso-based screening step followed by low-dimensional mean-variance optimization on the selected assets improves high-dimensional portfolio construction, with a defactoring extension for strong factors.