MedAI #46: Generative models with domain knowledge for weakly supervised clustering | Laura Manduchi

Title: Incorporating domain knowledge in deep generative models for weakly supervised clustering with applications to survival data Speaker: Laura Manduchi Abstract: The ever-growing amount of data and the time cost associated with its labeling have made clustering a relevant task in machine learning. Yet, in many cases, a fully unsupervised clustering algorithm might naturally find a solution that is not consistent with the domain knowledge. Additionally, practitioners often have access to prior information about the types of clusters that are sought, and a principled method to guide the algorithm towards a desirable configuration is then needed. This talk will explore how to integrate domain knowledge, in the form of pairwise constraints and survival data, in deep generative models. Leveraging side information in biomedical datasets enables exploratory analysis of complex data types, resulting in medically meaningful findings. Speaker Bio: Laura is a PhD student in Computer Science at the Institute of Ma