The rapid recent progress in machine learning (ML) has raised a number of scientific
questions that challenge the longstanding dogma of the field. One of the most important …

Interpolators—estimators that achieve zero training error—have attracted growing attention
in machine learning, mainly because state-of-the art neural networks appear to be models of …

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 …

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 …

We compare classification and regression tasks in an overparameterized linear model with
Gaussian features. On the one hand, we show that with sufficient overparameterization all …

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 …

Researchers investigating example hardness have increasingly focused on the dynamics by
which neural networks learn and forget examples throughout training. Popular metrics …

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 …

Conventional statistical wisdom established a well-understood relationship between model
complexity and prediction error, typically presented as a _U-shaped curve_ reflecting a …

Deep neural networks (DNNs) have greatly advanced the ability to predict genome function
from sequence. However, elucidating underlying biological mechanisms from genomic …