Special Session on “Complex Data: Learning Trustworthily, Automatically, and with Guarantees” @ ESANN 2021

Organized by Luca Oneto (University of Genoa, Italy), Nicolò Navarin (University of Padua, Italy), Battista Biggio (University of Cagliari, Italy), Federico Errica (Università di Pisa, Italy), Alessio Micheli (Università di Pisa, Italy), Franco Scarselli (SAILAB – University of Siena, Italy), Monica Bianchini (SAILAB – University of Siena, Italy), Alessandro Sperduti (University of Padua, Italy)
Machine Learning (ML) achievements enabled automatic extraction of actionable information from data in a wide range of decision-making scenarios (e.g. health care, cybersecurity, and education). ML models
are nowadays ubiquitous pushing even further the process of digitalization and datafication of the real and digital world producing more and more complex and interrelated data. This demands for improving both
ML technical aspects (e.g. design and automation) and human-related metrics (e.g. fairness, robustness, privacy, and explainability), with performance guarantees at both levels.

The aforementioned scenario posed three main challenges: (i) Learning from Complex Data (i.e. sequence, tree and graph data), (ii) Learning Trustworthily, and (iii) Learning Automatically with Guarantees. The scope of this special session is then to address one or more of these challenges with the final goal of Learning Trustworthily, Automatically, and with Guarantees from Complex Data.

Examples of methods and problems in these challenges are:

• • efficient and effective models capable of directly learning from data natively structured or collected from interrelated heterogeneous sources (e.g. social and relational data, knowledge graphs), characterized by entities, attributes, and relationships, without relying on human skills to encode this complexity into a rich and expressive (vectorial) representation;
  • design ML models from a human-centered perspective, making ML trustworthy by design, by removing human biases from the data (e.g. gender discrimination), increasing robustness (e.g. to adversarial data perturbation), preserving individuals’ privacy (e.g. protecting ML models from differential attacks), and increasing transparency (e.g. via ML models and output explanation);
  • automatizing the ML design and deployment parts which are currently handcrafted by highly skilled and trained specialists. For this reason, ML is required to be empowered with self-tuning properties (e.g. architecture and hyperparameter automatic selection), understanding and guaranteeing the final performance (e.g. with worst case and statistical bounds) with respect to both technical and human relevant metrics.

The focus of this special session is to attract both solid contributions or preliminary results which show the potentiality and the limitations of new ideas, refinements, or contaminations between the different fields of machine learning and other fields of research in solving real world problems. Both theoretical and practical results are welcome to our special session.