Deep generative models for tree-structured data with applications to molecular graphs - Do, Viet Anh (2024)
This thesis develops an autoregressive generative model for tree-structured data, extending sequential modeling principles to handle hierarchical structures while maintaining structural validity. Building on the Hierarchical Multiple Instance Learning (HMill) framework, we introduce a novel probability factorization that respects both ordered and unordered relationships in tree structures. Our neural implementation combines efficient state management with probability computation, enabling generation of complex hierarchical data like molecular structures. The model's architecture features bidirectional transformations between data and probability spaces, with careful handling of batched computation across variable-size structures. Experimental validation on the mutagenesis dataset demonstrates the model's ability to generate diverse, valid molecular structures while capturing meaningful chemical patterns. Our approach bridges an important gap between supervised and generative learning for hierarchical data, providing both theoretical insights and practical tools for working with tree-structured representations.