On intelligenCE And Networks
OCEAN aims to develop decentralized machine learning frameworks for incentive-driven agents, enhancing data handling and decision-making in competitive environments while addressing privacy and efficiency issues.
Projectdetails
Introduction
Until recently, most of the major advances in machine learning and decision making have focused on a centralized paradigm in which data are aggregated at a central location to train models and/or decide on actions. This paradigm faces serious flaws in many real-world cases.
Challenges of Centralized Learning
In particular, centralized 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.
Need for Decentralized Learning
It thus appears most timely to develop the theory and practice of a new form of machine learning that targets heterogeneous, massively decentralized networks, involving self-interested agents who expect to receive value (or rewards, incentive) for their participation in data exchanges.
OCEAN Project Overview
OCEAN will develop statistical and algorithmic foundations for systems involving multiple incentive-driven learning and decision-making agents, including uncertainty quantification at the agent's level.
Market Interaction
OCEAN will study the interaction of learning with market constraints, including:
- Scarcity
- Fairness
This will connect adaptive microeconomics and market-aware machine learning.
Foundations and Expertise
OCEAN builds on a decade of joint advances in:
- Stochastic optimization
- Probabilistic machine learning
- Statistical inference
- Bayesian assessment of uncertainty
- Computation
- Game theory
- Information science
PIs have complementary and internationally recognized skills in these domains.
Goals and Impact
OCEAN will shed new light on the value and handling of data in a competitive, potentially antagonistic multi-agent environment. It aims to develop new theories and methods to address these pressing challenges.
Conclusion
OCEAN requires a fundamental departure from standard approaches and leads to major scientific interdisciplinary endeavors that will transform statistical learning in the long term while opening up exciting and novel areas of research.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 7.762.668 |
Totale projectbegroting | € 7.762.668 |
Tijdlijn
Startdatum | 1-3-2023 |
Einddatum | 28-2-2029 |
Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- ECOLE POLYTECHNIQUEpenvoerder
- UNIVERSITE PARIS DAUPHINE
- THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
- GROUPE DES ECOLES NATIONALES D ECONOMIE ET STATISTIQUE
- UNIVERSITY OF WARWICK
- UNIVERSITY OF NEWCASTLE UPON TYNE
- UNIVERSITY OF DURHAM
- UNIVERSITY OF ESSEX
Land(en)
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