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Graph Neural Networks (GNNs) have emerged as a fundamental building block in many artificial intelligence systems. Even beyond uses where the graph structure is explicit (e.g. social networks), they show impressive performance for general object-oriented perception, representation, and reasoning. In this seminar we will cover GNNs that are not only accurate or efficient, but also robust, privacy-preserving, fair, uncertainty-aware, and explainable. We will explore how GNNs fail w.r.t. these trustworthiness aspects and how to improve them.
Organization (block format):
Each student will receive a few research papers on a single topic which they should carefully read and analyze. Starting from these initial papers they should explore the surrounding literature and summarize their main ideas and findings in a 4-page seminar paper. Students will also participate in a peer-review process where they have to provide constructive feedback on each other's work (1 page review for 3 other papers). Finally, each student will prepare and deliver a presentation about their topic during a block seminar at the end of the semester.
- Seminar paper (40%)
- Presentation (30%)
- Reviews (30%, 10% for each review)
- Kick-off meeting at the start of the semester (online via Zoom)
- Deadline for the first draft of the seminar paper
- Deadline for the final version of the seminar paper
- Deadline for the reviews
- Feedback round / practice talk with your supervisor
- Final presentations at the end of the semester
Exact dates and times will be determined soon.
Preliminary list of topics:
- Adversarial attacks
- Heuristic defenses
- Provable defenses
- Privacy attacks
- Privacy-preserving models
- Instance-level explanations
- Model-level explanations
- Uncertainty-aware models
More details and the final list of topics will be provided in the kick-off meeting.
You should attend this seminar to:
- Explore and learn about state-of-the-art research on Graph Neural Networks
- Improve your scientific writing
- Improve your presentation skills
- Participate in a review process akin to international conferences