Objective
The recent success of machine learning (ML) has been fueled by black-box supervised learning on massive amounts of data. While this has led to many applications throughout science, engineering, and industry, new areas such as healthcare, transport, or robotics require interactions between predictions and decisions from high-dimensional data, such as in reinforcement learning (RL), and provably well-calibrated uncertainty estimates.
This project aims to address these requirements with the same reliability and efficiency as supervised learning, which current RL algorithms do not possess. This will be achieved by opening the black box at an appropriate granularity and proposing a flexible, unified algorithmic framework based on probabilistic graphical models. The revival of this classical modeling tool presents significant, radically novel scientific challenges when dealing with high-dimensionality and nonlinearity: (1) ML must interact with partially observed environments or other agents; (2) theoretical guarantees are essential but remain largely underdeveloped, both a priori, to characterize the amount of resources needed for a particular problem, and a posteriori, to certify the performance of an algorithm on unseen data.
So-called influence diagrams have been precisely defined to extend graphical models by adding action and utility nodes. Their development, however, has so far been limited to the simplest white-box situations where all components of the learning systems have to be explicitly modeled. In order to tackle modern applications, we will leverage and extend the latest tools from ML and optimization, such as score-based generative modeling and sum-of-squares optimization. These new frameworks open a unique window of opportunity for a gray-box approach that models data at the right level.
This project will result in a suite of learning algorithms and open-source software coming with theoretical guarantees and a wide range of applications.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence machine learning supervised learning
- natural sciences computer and information sciences software
- natural sciences computer and information sciences artificial intelligence machine learning reinforcement learning
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering robotics
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2025-ADG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
78153 Le Chesnay Cedex
France
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.