Dear all, this is a reminder for Christopher Morris's talk with the title "Understanding the Generalization Abilities of Graph Neural Networks: Current Results and Future Directions" taking place today (11.07) at 12:30 in the B-IT room 5053.2. Please find the details below --- Abstract --- Graph neural networks (GNNs) are the dominant approach in machine learning on graphs. Surprisingly, their generalization properties, i.e., their ability to make meaningful predictions outside the training data, are poorly understood. Here, we overview some recent results on GNNs’ generalization abilities and outline some future directions. ---------------- Part of the programme of the research training group UnRAVeL is a series of lectures on the topics of UnRAVeL’s research thrusts algorithms and complexity, verification, logic and languages, and their application scenarios. Each lecture is given by one of the researchers involved in UnRAVeL. This years topic is "UnRAVeL - New Ideas!". In these lectures, UnRAVeL professors will discuss current research as well as highlight open problems and offer a perspective on potential future directions. All interested doctoral researchers and master students are invited to attend the UnRAVeL lecture series 2024 and engage in discussions with researchers and doctoral students. We are looking forward to seeing you at the lectures. Kind regards, Jan-Christoph for the organisation committee