ERC OCEAN - On IntelligenCE And Networks

About

OCEAN is an ERC Synergy grant gathering researchers from diverse backgrounds to provide the foundation for the next generation of machine learning algorithms, where multiple agents engage over a network, operate over lengthy stretches of time in a shared environment, and engage in interactions that may be either collaborative or competitive.

The Project

Machine learning and artificial intelligence (AI) have made major strides in the last two decades. The progress has been based on a dramatic increase of data and computing capacity, in the context of a centralized paradigm that requires aggregating data in a single location where massive computing resources can be brought to bear.

This fully centralized machine learning paradigm is, however, increasingly at odds with realworld use cases, for reasons that are both technological and societal. In particular, centralised learning risks exposing user privacy, makes inefficient use of communication resources, creates data processing bottlenecks, and may lead to concentration of economic and political power.

It thus appears most timely to develop the theory and practice of a new form of machine learning that targets heterogeneous, massively decentralised networks, involving self-interested agents who expect to receive value (or rewards, incentive) for their participation in data exchanges.

In response to these challenges, OCEAN is an ERC-funded project which aims to develop statistical and algorithmic foundations for systems involving multiple incentive-driven learning and decision-making agents, including uncertainty quantification predominantly with a Bayesian focus. OCEAN will study the interaction of learning with market constraints (scarcity, fairness, privacy), connecting adaptive microeconomics and market-aware machine learning. To achieve these goals, OCEAN will need to develop new statistical and machine-learning methodologies, together with algorithms for sampling and optimisation which are both scalable to large problems, and have provable theoretical guarantees.

Key Research Themes:

Statistical inference and prediction within large network of agents
Economic value of data and welfare-maximizing mechanisms in the presence of rational agents
Autonomous and adaptative decision-making within time-varying environments

Discover all the latest updates about the project directly on the dedicated project website !

The project is supported by :

“Funded by the European Union (ERC-2022-SYG-OCEAN-101071601). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.”

Consortium

Principal Investigators

Eric Moulines, Professeur, Ecole polytechnique, France.
Michael I. Jordan, Professeur, Professeur, UC Berkeley, États-Unis.
Christian P. Robert, Professeur, Université Paris-Dauphine PSL, France.

Associated Partners

Gareth Roberts, Professeur, Université Warwick, Royaume-Unis.
Murray Pollock, Maître de Conférences, Université Newcastle, Royaume-Unis.
Louis Aslett, Professeur Associé, Université Durham, Royaume-Unis.
Vianney Perchet, Professeur, Centre de recherche en économie et statistique (CREST) ENSAE, France.