Project description
Accuracy and speed in AI
Unsupervised learning enables AI to uncover hidden patterns in data without human guidance. It does this by searching for patterns in unlabelled data, such as clustering customers to ranking preferences. However, current methods require too much computer power. In some cases, small changes in a model can break old assumptions. The ERC-funded SOCE project aims to address these limits. It will study fundamental trade-offs between statistical accuracy and computational efficiency, across problems such as robust clustering and ranking. Moving beyond batch settings, SOCE will examine active, sequential learning under sampling constraints. By developing new mathematical tools and algorithms, the goal is to clarify what can and cannot be learned from complex data.
Objective
Unsupervised learning is a key problem of artificial intelligence, at the crossroad of statistics and machine learning. The aim is to infer patterns from unlabelled data, by providing learning algorithms that are computationally efficient - i.e. polynomial time - and statistically performant - i.e. minimising an error criterion - and by characterising the fundamental limits for learning.
In the last decade, deep and important phenomena of statistical-computational trade-offs have been unveiled: for some canonical vanilla problems, it is now admitted that no algorithm is both statistically optimal and computationally efficient. However, and somewhat surprisingly, many extensions of these commonly admitted conjectures to other models that present slight variations have been recently proven wrong. The reason is that these model variations give rise to additional structure. This could be a blessing if it can be exploited by a well chosen computationally efficient algorithm, or a curse if it confuses any such algorithm. So that many
fundamental unsupervised learning problems, like robust or hierarchical clustering as well as ordering models like ranking or seriation, are poorly understood.
Beyond this, in modern applications like recommender systems, unsupervised learning is often done in a sequential active way, as a complement to batch learning. Yet, efficient algorithms and the understanding of their limits are also vastly lacking, in particular in the presence of additional structure. And active learning is often done under sampling constraints, which adds a layer of model variations with respect to batch learning.
In SOCE, I will tackle these complex unsupervised learning problems, which are not well understood despite their importance. I will go from batch to active unsupervised learning, and study their interface through sampling constraints. I will develop new mathematical tools and algorithms that will be instrumental for a systematic study of these problems.
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.
-
HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
See all projects funded under this programme
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
See all projects funded under this funding scheme
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-COG
See all projects funded under this callHost institution
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.
14469 Potsdam
Germany
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.