Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

The notion of propagation of chaos for large systems of interacting particles originates in
statistical physics and has recently become a central notion in many areas of applied …

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 …

In deep learning theory, the covariance matrix of the representations serves as aproxy to
examine the network's trainability. Motivated by the success of Transform-ers, we study the …

A longstanding goal in deep learning research has been to precisely characterize training
and generalization. However, the often complex loss landscapes of neural networks have …

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 …

Gradient descent finds a global minimum in training deep neural networks despite the
objective function being non-convex. The current paper proves gradient descent achieves …

Many tasks in machine learning and signal processing can be solved by minimizing a
convex function of a measure. This includes sparse spikes deconvolution or training a …

Multilayer neural networks are among the most powerful models in machine learning, yet the
fundamental reasons for this success defy mathematical understanding. Learning a neural …