Luca Oneto, Nicolo Navarin, Alessio Micheli, Luca Pasa, Claudio Gallicchio, Davide Bacciu, Davide Anguita, Informed Machine Learning for Complex Data, in ESANN 2024 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium) and online event, 9-11 October 2024.
Abstract: 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., Large Language Models and Graph Neural Networks). 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 sustainability, domain experts require ML to be aligned with physical and logical laws, and society requires ML to be aligned with ethical principles. This special session gathers valuable contributions and early findings in the field of Informed ML for Complex Data. Our main objective is to showcase the potential and limitations of new ideas, improvements, or the blending of ML and other research areas in solving real-world problems.
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