Benign overfitting, the phenomenon where interpolating models generalize well in the
presence of noisy data, was first observed in neural network models trained with gradient …

Linear classifiers and leaky ReLU networks trained by gradient flow on the logistic loss have
an implicit bias towards solutions which satisfy the Karush–Kuhn–Tucker (KKT) conditions …

We study a localized notion of uniform convergence known as an “optimistic rate”[,] for linear
regression with Gaussian data. Our refined analysis avoids the hidden constant and …

We prove a new generalization bound that shows for any class of linear predictors in
Gaussian space, the Rademacher complexity of the class and the training error under any …

This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU
activation functions and proves that when the data distribution is well-separated, DNNs can …

We bound the excess risk of interpolating deep linear networks trained using gradient flow.
In a setting previously used to establish risk bounds for the minimum ℓ2-norm interpolant, we …

We prove a lower bound on the excess risk of sparse interpolating procedures for linear
regression with Gaussian data in the overparameterized regime. We apply this result to …

We study the asymptotic generalization of an overparameterized linear model for multiclass
classification under the Gaussian covariates bi-level model introduced in Subramanian et …

Via an overparameterized linear model with Gaussian features, we provide conditions for
good generalization for multiclass classification of minimum-norm interpolating solutions in …

We study the asymptotic overfitting behavior of interpolation with minimum norm ($\ell_2 $ of
the weights) two-layer ReLU networks for noisy univariate regression. We show that …