Objective
Generative modeling, the automatic generation of examples such as texts, images, music, and molecules that are similar to those in a given dataset, is a central task in artificial intelligence. Mathematically, this task is framed as the problem of sampling from an unknown distribution, which is accessible only through a limited set of examples drawn from it. The size and quality of this set can vary greatly depending on the application. The algorithms that have propelled generative modeling to fame are known for their substantial data and computational resource requirements, often necessitating vast amounts of both to achieve state-of-the-art performance.
The goal of this project is to investigate the mathematical properties of generative modeling algorithms to better understand their strengths and weaknesses, enhance their efficiency, and design new methods. The mathematical challenge in generative modeling lies in successfully integrating techniques from various areas of mathematical statistics and probability theory: dimension reduction, nonparametric estimation, manifold learning, sampling, optimal transport, stochastic calculus, etc. Investigating the mathematical properties of this pipeline requires a deep analysis of these methods and their interactions to solve the overarching problem. Such analysis is key to exploring multiple facets of generative modeling algorithms, including precision, robustness, creativity, and computational traceability.
Our focus will be on obtaining interpretable statistical guarantees that highlight the impact of sample size, intrinsic and ambient dimensions, noise level, and contamination rate on precision, creativity, and running time. These guarantees are essential in AI to ensure the reliability of the resulting algorithms and enhance their trustworthiness, explainability, and frugality. We will pay special attention to stability and robustness properties, particularly against model misspecification, noise, and outliers.
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.
This project's classification has been human-validated.
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.
This project's classification has been human-validated.
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-2024-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.
91120 Palaiseau
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.