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math.ST2025
The Lasso error is bounded iff its active set size is bounded away from n in the proportional regime
Pierre C. Bellec
This note develops an analysis of the Lasso \( \hat b\) in linear models without any sparsity or L1 assumption on the true regression vector, in the proportional regime where dimen…
math.ST2025★ 1 cited
Phase transitions for the existence of unregularized M-estimators in single index models
Takuya Koriyama, Pierre C. Bellec
This paper studies phase transitions for the existence of unregularized M-estimators under proportional asymptotics where the sample size and feature dimension grow proport…
math.ST2025★ 1 cited
Simultaneous analysis of approximate leave-one-out cross-validation and mean-field inference
Pierre C Bellec
Approximate Leave-One-Out Cross-Validation (ALO-CV) is a method that has been proposed to estimate the generalization error of a regularized estimator in the high-dimensional regim…