ℓ₂-Boosting exhibits benign overfitting with logarithmic excess variance decay Θ(σ²/log(p/n)) under isotropic noise due to ℓ₁ bias, and a subdifferential early stopping rule recovers minimax-optimal ℓ₁ rates.
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Partitioned Gaussian sketching for distributed OLS has exact excess loss B_θ that is comparable to whole-data sketching when subset-covariance divergence D is near d.
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When Does $\ell_2$-Boosting Overfit Benignly? High-Dimensional Risk Asymptotics and the $\ell_1$ Implicit Bias
ℓ₂-Boosting exhibits benign overfitting with logarithmic excess variance decay Θ(σ²/log(p/n)) under isotropic noise due to ℓ₁ bias, and a subdifferential early stopping rule recovers minimax-optimal ℓ₁ rates.
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Distributed Sketching on Data Partitions for OLS Regression
Partitioned Gaussian sketching for distributed OLS has exact excess loss B_θ that is comparable to whole-data sketching when subset-covariance divergence D is near d.