Scaling Hybrid Probabilistic Inference with Logical Constraints by Relaxing and Compensating
Research Scholar, StarAI Lab, UCLA, 2019
- Advisor: Prof. Guy Van den Broeck
- Date: Sep 2019 - PRESENT
- Proposed a novel algorithm to approximate Model Integration (MI) inference within the RCR framework
- Devised various update rules for iterative optimization scheme in the compensation step, including probability matching and moment matching
- Analyzed the convergence property for update equations when the relaxed equivalence constraint both connect and disconnect the primal graph, using the fixed-point theorem