News
Announcement: Assignment Topics Confirmed – Thank You for Your Selections!
Written on 01.11.2024 09:26 by Hanwei Zhang
Hey all,
Thank you all for taking the time to complete the topic bidding process! Your thoughtful selections reflect a strong interest in advancing our understanding of model explainability and interpretability, and we’re excited to see the unique perspectives you’ll bring to these advanced topics.
Here are your confirmed assignments:
- Juan José Valenzuela González– LIME: Model-agnostic Local Explanations for Complex Models
- Tulika Nayak – SHAP: Feature Importance Using Shapley Values from Game Theory
- Shashank Priyadarshi – LRP: Layer-wise Relevance Propagation
- Nhi Pham – Counterfactual Explanations: “What-if” Scenarios for Actionable Insights
- Mayur Deshmukh – Anchors: Rule-based, High-Precision Explanations for Stability
- Shreyansh Tripathi – Graph Neural Networks Explainability: Explaining GNNs for Structured Data
- Maurice Vincon – Concept Activation Vectors (TCAV): High-Level, Human-Friendly Explanations
Now that topics are finalized, please start by reviewing the assigned paper, available through the links provided in the slides from our kick-off course. As you investigate the paper and related concepts in explainable AI, gather relevant information to structure your research. Then, begin developing your assignments, focusing on clarity, depth, and originality.
Resources & Support
Slides are available in the course materials section on the website. If you have any questions or need further support, please don’t hesitate to reach out!
Important:
Please register for the "examination" in the LSF by November 22 to ensure your participation is confirmed.
Thank you once again for your participation in the topic selection process. I am eager to see the insights and creativity each of you will bring to these exciting areas of machine learning explainability.
Best regards,
Hanwei