Machine Learning is very successful in many aspects of daily life. My PhD, in statistical mechanics of learning aims to give some theoretical understanding of its success.

In one line of research, I work towards understanding the generalisation properties of over-parametrised neural network. For example, one of my works aims to characterise the role of the various sources of noise in the decrease of the test error in the over-parametrised regime. Another work, compares the performances of lazy methods such as kernel methods to those of neural networks on synthetic datasets.

I am also interested in describing the dynamics of Neural Networks. This allows to study alternative training algorithm, such as direct feedback alignment which tries to circumvent some of the pitfall of vanilla back propagation and is more biologically plausible. I am also working towards applying these dynamical methods to study toy models of unsupervised learning such as non-linear auto-encoders.

If you want to know more scroll down to some view some talks, read my publications or email me at mariaref@gmail.com .