Jianhua Guo

2 accepted papers

2025

An Improved Clique-Picking Algorithm for Counting Markov Equivalent DAGs via Super Cliques Transfer

ICML 2025oral

Efficiently counting Markov equivalent directed acyclic graphs (DAGs) is crucial in graphical causal analysis. Wienöbst et al. (2023) introduced a polynomial-time algorithm, known as the Clique-Picking algorithm, to count the number of Markov equivalent DAGs for a given completed partially directed…

Cited by 0 Source ↗PDF ↗Scholar ↗

2025

DSCS: Fast CPDAG-Based Verification of Collapsible Submodels in High-Dimensional Bayesian Networks

Wentao Wu, Shiyuan He, Jianhua Guo

NeurIPS 2025poster

Bayesian networks (BNs), represented by directed acyclic graphs (DAGs), provide a principled framework for modeling complex dependencies among random variables. As data dimensionality increases into the tens of thousands, fitting and marginalizing a full BN becomes computationally prohibitive—partic…

Cited by 0 Source ↗Scholar ↗