Objective
Massive data poses a fundamental challenge to machine learning (ML) since algorithm running times scale
with data size. As data grows faster than computational power, future problems can take longer to solve
without economizing training via continual learning (CL). However, CL can result in performance drops in
prior tasks, known as catastrophic forgetting (CF), a persistent issue in ML, including fine-tuning foundation
models (FMs), blocking their full potential.
Contrary to the dogma that increasing more resources are critical for future FMs, our recent research
shows that computational investments can be recovered near-optimally without CF, hinting at a sustainable,
future-proof ML paradigm. Our observation calls for new formulations in robust representation learning
that prevent catastrophic overfitting (CO) and new game-theoretic algorithms that avoid limit cycles (LC).
However, understanding the trade-offs between data and computation in CL is at its infancy, especially for
non-linear models. Existing methods are case-specific and do not rigorously offer sustainability, robustness, or
trustworthiness.
The proposed ERC project ARC addresses these challenges by establishing mathematical foundations to
manage continuous data growth without CF or CO or LC and to optimize dynamic representations with
new robust and reinforcement learning formulations. We build new adaptive algorithms as well as decision
verification techniques that optimally handle increasing data sizes as well as dynamic models while matching
theoretical runtime bounds. We demonstrate our rigorous theory and robust learning frameworks in real-world
scenarios, such as neuro-rehabilitation and circuit design, to validate methods and explore trade-offs across
domains.
Our approach views learning problems as dynamic and interactive, potentially revolutionizing ML by creating
holistic, sustainable systems with optimal performance across diverse applications under limited resources.
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 reinforcement learning
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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.
1015 Lausanne
Switzerland
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.