Efficient Search-Based Weighted Model Integration

Runtime on House Price Problems


Weighted model integration (WMI) extends weighted model counting to integration in mixed discrete-continuous domains. It has shown tremendous promise for solving probabilistic inference problems in graphical models and probabilistic programs. Yet, state-of-the-art tools for WMI have limited performance and ignore the independence structure that is crucial to improving efficiency. To address this limitation, we propose an efficient model integration algorithm for theories with tree pri-mal graphs. We exploit the sparse graph structure by using search to performing integration. Our algorithm greatly improves the computational efficiency on such problems and exploits context-specific independence between variables. Experimental results show dramatic speedups compared to existing WMI solvers on problems with tree-shaped dependencies.

In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019)
Zhe Zeng
Zhe Zeng
Ph.D. student in AI

My research goal is to enable machine learning models to incorporate diverse forms of constraints into probabilistic inference and learning in a principled way, by combining machine learning (probabilistic modeling, neuro-symbolic AI, Bayesian deep learning) and formal methods.