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Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.
Explores the theoretical aspects of UX audit, expert evaluation, and benchmarking, emphasizing the importance of detecting design issues impacting usability and user experience.
Explores the intersection of machine learning and privacy, discussing confidentiality, attacks, differential privacy, and trade-offs in federated learning.
