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 …

Psychiatry today must gain a better understanding of the common and distinct
pathophysiological mechanisms underlying psychiatric disorders in order to deliver more …

The remarkable practical success of deep learning has revealed some major surprises from
a theoretical perspective. In particular, simple gradient methods easily find near-optimal …

Deep learning algorithms are well-known to have a propensity for fitting the training data
very well and often fit even outliers and mislabeled data points. Such fitting requires …

In the past decade the mathematical theory of machine learning has lagged far behind the
triumphs of deep neural networks on practical challenges. However, the gap between theory …

The phenomenon of benign overfitting is one of the key mysteries uncovered by deep
learning methodology: deep neural networks seem to predict well, even with a perfect fit to …

Deep learning methods operate in regimes that defy the traditional statistical mindset.
Neural network architectures often contain more parameters than training samples, and are …

Breakthroughs in machine learning are rapidly changing science and society, yet our
fundamental understanding of this technology has lagged far behind. Indeed, one of the …

Recent works have cast some light on the mystery of why deep nets fit any data and
generalize despite being very overparametrized. This paper analyzes training and …

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 …