Interactive Machine Learning with Graph-Structured Data
The Austrian Computer Science Day (ACSD) is an annual event that brings together computer scientists from across Austria and beyond to enhance the visibility of the field and promote collaboration. The program features scientific presentations from both established and early-career researchers. The 2024 edition focuses on the theme “Networks in Artificial Intelligence.”
Prof. Thomas Gärtner delivered a lecture titled “Interactive Machine Learning with Graph-Structured Data.”
Abstract: In this talk I will give an overview of our contributions to what I call interactive machine learning. Often, interaction in Computer Science is interpreted as the interaction of humans with the computer but I intend a broader meaning of the interaction of machine learning algorithms with the real world, including but not restricted to humans. Interactions with humans span a broad range, where they can be intentional and guided by the human or they can be guided by the computer such that the human is oblivious of being guided. Another example of an interaction with the real world is the use of machine learning algorithms in cyclic discovery processes such as drug design. Important properties of interactive machine learning algorithms include efficiency, effectiveness, responsiveness, and robustness. In the talk I will show how these can be achieved in a variety of interactive contexts, focussing on graph-structured data.
Petra Hozzová, PhD student in the Automated Program Reasoning group, participated in the Special Session “Young Experts – Minute Madness” with her presentation “Inductive Reasoning in Superposition.” In this session, outstanding doctoral students in computer science from Austrian universities delivered 1-minute overview talks showcasing their research, followed by an interactive poster session.