**Argo** is a research team at the interface of computer science and applied mathematics. We are part of **INRIA Paris** and the **DI ENS** (computer science department of Ecole Normale SupĂ©rieure de Paris), and a spin-off of the Dyogene project-team. The research activity of **Argo** focuses on **learning**, **optimization** and **control methods** for **graphs and networks**. Our main applications are social networks and energy networks.

## Research directions

The challenges we aim to address are:

**Determine efficient polynomial-time algorithms for fundamental graph processing tasks** such as clustering and graph alignment; advance understanding of the "hard phase" for such graph problems which consists of problem instances with no known polynomial time algorithms while non-polynomial time algorithms are known to exist.

**Develop new deep learning architectures.** We envision to use graph theory and algorithms to better understand and improve neural networks, either by reducing their size or by enhancing their structure. Message passing is the dominant paradigm in Graphical Neural Networks (GNN) but has fundamental limitations due to its equivalence to the Weisfeiler-Lehman isomorphism test. We will investigate architectures breaking away from the message-passing schemes to develop more expressive GNN architectures.

**Develop distributed algorithms for federated learning**, achieving optimal performance for: supervised learning of a common model, under constraints of privacy and energy consumption; personalized learning of individual models; unsupervised learning of clustering and mixture models.

**Advance the theory of reinforcement learning** by investigating convergence properties and connections with control theory. We also envision to develop new reinforcement learning algorithms for distributed systems.