PhD Student


amaury.triboulin [at]

Short bio

Master’s Degree at Ecole normale supérieure

Thesis title

Symmetries in Machine Learning for Structured Data.

Short abstract

In this thesis, we will consider high-dimensional problems with an additional structure that comes from the geometry of the input signal and explore ways to incorporate this geometric structure into the learning algorithms. We have already started to investigate new architectures based on equivariant layers which we tested on combinatorial optimization problems and showed that it is possible learn representations of hard (typically NP-hard) problems. We believe this could lead to new algorithms, less resource-dependant, for learning efficient heuristics for practical instances.