Modern neural networks often have great expressive power and can be trained to overfit the
training data, while still achieving a good test performance. This phenomenon is referred to …

The practical success of overparameterized neural networks has motivated the recent
scientific study of\emph {interpolating methods}--learning methods which are able fit their …

Modern deep learning models with great expressive power can be trained to overfit the
training data but still generalize well. This phenomenon is referred to as benign overfitting …

The implicit bias towards solutions with favorable properties is believed to be a key reason
why neural networks trained by gradient-based optimization can generalize well. While the …

The challenge of overfitting, in which the model memorizes the training data and fails to
generalize to test data, has become increasingly significant in the training of large neural …

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 …

In this work, we study the implications of the implicit bias of gradient flow on generalization
and adversarial robustness in ReLU networks. We focus on a setting where the data …

The implicit biases of gradient-based optimization algorithms are conjectured to be a major
factor in the success of modern deep learning. In this work, we investigate the implicit bias of …

Mixup, a simple data augmentation method that randomly mixes two data points via linear
interpolation, has been extensively applied in various deep learning applications to gain …

The phenomenon of benign overfitting, where a predictor perfectly fits noisy training data
while attaining low expected loss, has received much attention in recent years, but still …