Funded project (January 2023 - December 2026)
Learning with Multiple Representations
Specifically, LMR algorithms will allow flexible representations (e.g., suitable for explainability, fairness, …) with diverse target functions (e.g., incorporating environmental or even social impact) so as to make the induced models abide by the Green Charter and trustworthy AI criteria by design. The project will focus on learning with weak supervision because it addresses one of the major flaws of modern ML approaches, i.e., their data hunger, by means of weaker sources of labeling for training data. The outcome of the DN will be a set of 10 experts trained to implement the third and subsequent waves of AI in Europe. The highly interdisciplinary and intersectoral context in which they will be trained will provide them with research-related and transferable competencies relevant to successful careers in central AI areas.
Journal of Machine Learning Research, JMLR, 2025, #article
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, 2024, #inproceedings
Companion Proceedings of the ACM on Web Conference 2024, 2024, #inproceedings
Proceedings of the 27TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2024, #inproceedings
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024, #inproceedings
Journal of Machine Learning Research, JMLR, 2025, #article
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, 2024, #inproceedings
Companion Proceedings of the ACM on Web Conference 2024, 2024, #inproceedings
Proceedings of the 27TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2024, #inproceedings
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024, #inproceedings