Ο Χρήστος Τζάμος, Αναπληρωτής Καθηγητής του Τμήματος Πληροφορικής και Τηλεπικοινωνιών στο ΕΚΠΑ, λαμβάνει από το Ευρωπαϊκό Συμβούλιο Έρευνας (ERC) την επιχορήγηση Εδραίωσης (Consolidator grants) για το έργο «COMFORMAL» για την αξιόπιστη μηχανική μάθηση.
ERC CoG: COMFORMAL - Computational Foundations of Reliable Machine Learning
Machine learning has seen huge advances in recent years, achieving super-human performance in complex tasks like image classification, text generation, and playing games. Despite this success, however, its applicability across industries is still limited as real-world data is often noisy, incomplete, or scarce, and reliability is paramount. To realize the full potential and extend the reach of machine learning, we need improved methods with performance guarantees that can reliably solve complex tasks under limited guidance and in the face of these real-world challenges. This project will develop such methods by focusing on establishing a rigorous theoretical framework for robust and efficient learning.
Our research will center around two interconnected core themes: learning under noisy data and learning via interactive queries. The first theme will develop algorithms capable of learning effectively from imperfect data, addressing the challenge of noise that is ubiquitous in real-world
applications. The second theme will explore the power of interactive learning, leveraging strategically designed queries to drastically improve learning efficiency, especially in scenarios with limited labeled data.
A third, crucial direction will focus on connecting theory and practice, ensuring that the theoretical findings translate into tangible improvements in real-world applications. This will be achieved via the development of novel, synthetic data generators and targeted benchmarks,
directly informed by theory. These tools will facilitate the adoption of theoretical advances, demonstrating limitations of existing AI methods and suggesting ways for improvement. This research will pave the way for more robust and trustworthy AI systems capable of tackling critical challenges in diverse fields such as healthcare, autonomous systems, and beyond, where reliability is essential.