2 papers
cs.LG2026
Smoothing the Landscape: Causal Structure Learning via Diffusion Denoising Objectives
Hao Zhu, Di Zhou, Donna Slonim
Understanding causal dependencies in observational data is critical for informing decision-making. These relationships are often modeled as Bayesian Networks (BNs) and Directed Acy…
cs.LG2025
Graph Self-Supervised Learning with Learnable Structural and Positional Encodings
Asiri Wijesinghe, Hao Zhu, Piotr Koniusz
Traditional Graph Self-Supervised Learning (GSSL) struggles to capture complex structural properties well. This limitation stems from two main factors: (1) the inadequacy of conven…