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Informed ML for Complex Data@ESANN 2024 : Informed Machine Learning for Complex Data special session at ESANN 2024


When Oct 9, 2024 - Oct 11, 2024
Where Bruges, Belgium
Submission Deadline May 2, 2024
Notification Due Jun 16, 2024
Categories    computer science   machine learning   data mining   artificial intelligence

Call For Papers

Call for papers: special session on "Informed Machine Learning for Complex Data" at ESANN 2024 -

European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2024).
9-11 October 2024, Bruges, Belgium -

In the contemporary era of data-driven decision-making, the application of Machine Learning (ML) on complex data (e.g., images, text, sequences, trees, and graphs) has become increasingly pivotal (e.g., LLM and GraphNN for Drugs Discovery). In this context, there is a gap between purely data-driven models and domain-specific knowledge, requirements, and expertise. In particular, this domain specificity needs to be integrated into the ML models to improve learning generalization, sustainability, trustworthiness, reliability, security, and safety. This additional knowledge can assume different forms, e.g.:
- software developers require ML to comply with many technical requirements;
- companies require ML to comply with economic and environmental
- domain experts require ML to be aligned with physical and logical laws;
- society requires ML to be aligned with ethical principles.
This special session aims to gather valuable contributions and early
findings in the field of Informed Machine Learning for Complex Data. Our
main objective is to showcase the potential and limitations of new ideas,
improvements, or the blending of Artificial Intelligence, Machine Learning,
and other research areas in solving real-world problems. We invite both
theoretical and practical results to this special session.

- Data-informed ML (e.g., the ability to directly learn from complex data)
- Technically-informed ML (e.g., regressiveness, replicability, and
- Sustainability-informed ML (e.g., ability to learn and predict
efficiently from data)
- Knowledge-informed ML (e.g., physical laws, logical requirements, and
- Ethically-informed ML (e.g., fairness, explainability, fairness, and
cultural competence)

Prospective authors must submit their paper through the ESANN portal following the instructions provided in
Each paper will undergo a peer reviewing process for its acceptance.

Submission of papers: 2 May 2024
Notification of acceptance: 16 June 2024
ESANN conference: 9-11 October 2024

Luca Oneto (University of Genoa, Italy)
Nicol? Navarin (University of Padua, Italy)
Alessio Micheli (University of Pisa, Italy)
Luca Pasa (University of Padova, Italy)
Claudio Gallicchio (University of Pisa, Italy)
Davide Bacciu (University of Pisa, Italy)
Davide Anguita (DIBRIS - University of Genova, Italy)

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