13 citations · 13 across the 7 of their papers we have counts for
9 papers
Sharp convergence rate of the estimation error of LASSO via the Feature Space Decomposition method
Guillaume Lecué, Zong Shang
In this paper, we use the Feature Space Decomposition (FSD) method to establish an explicit, non-asymptotic, instance-optimal convergence rate for the estimation error of the LASSO…
Benign Overfitting Property of the Minimum -norm Interpolant Estimator in Regression Problem via Feature Space Decomposition
Radosław Adamczak, Guillaume Lecué, Zong Shang +1
This paper establishes high-probability, non-asymptotic upper bounds for the population excess risk of minimum -norm interpolating estimators in linear regression, valid fo…
The Geometry of Statistical Feature Learning in Mean-Field Langevin Dynamics
Zong Shang, Tomoya Wakayama, Guillaume Lecué +1
We introduce a geometric formulation of statistical feature learning for supervised regression. Feature learning is defined through a base--fiber decomposition: the base is the fea…
Sharp convergence rates for Spectral methods via the feature space decomposition method
Guillaume Lecué, Zhifan Li, Zong Shang
In this paper, we apply the Feature Space Decomposition (FSD) method developed in [LS24, GLS25, LSSW26, ALSS26] to obtain, under fairly general conditions, matching upper and lower…
Statistical Inference in Large Multi-way Networks
Lucas Resende, Guillaume Lecué, Lionel Wilner +1
We propose the Polyads estimator, a new method to estimate structural parameters in weighted multi-way networks while controlling for rich, arbitrary structures of fixed effects. T…
A Geometrical Analysis of Kernel Ridge Regression and its Applications
Georgios Gavrilopoulos, Guillaume Lecué, Zong Shang
We obtain upper bounds for the estimation error of Kernel Ridge Regression (KRR) for all non-negative regularization parameters, offering a geometric perspective on various phenome…
Learning with a linear loss function. Excess risk and estimation bounds for ERM, minmax MOM and their regularized versions. Applications to robustness in sparse PCA
Guillaume Lecué, Lucie Neirac
Motivated by several examples, we consider a general framework of learning with linear loss functions. In this context, we provide excess risk and estimation bounds that hold with…
A geometrical viewpoint on the benign overfitting property of the minimum -norm interpolant estimator and its universality
Guillaume Lecué, Zong Shang
In the linear regression model, the minimum l2-norm interpolant estimator has received much attention since it was proved to be consistent even though it fits noisy data perfectly…
Empirical risk minimization is optimal for the convex aggregation problem
Guillaume Lecué
Let be a finite model of cardinality and denote by its convex hull. The problem of convex aggregation is to construct a procedure having a risk as…