Name
Abstract | ||
|---|---|---|
Probabilistic graphical models, such as Markov random fields (MRF), exploit dependencies among random variables to model a rich family of joint probability distributions. Inference algorithms, such as belief propagation (BP), can effectively compute the marginal posteriors for decision making. Nonetheless, inferences involve sophisticated probability calculations and are difficult for humans to interpret. Among all existing explanation methods for MRFs, no method is designed for fair attributions of an inference outcome to elements on the MRF where the inference takes place. Shapley values provide rigorous attributions but so far have not been studied on MRFs. We thus define Shapley values for MRFs to capture both probabilistic and topological contributions of the variables on MRFs. We theoretically characterize the new definition regarding independence, equal contribution, additivity, and submodularity. As brute-force computation of the Shapley values is challenging, we propose GraphShapley, an approximation algorithm that exploits the decomposability of Shapley values, the structure of MRFs, and the iterative nature of BP inference to speed up the computation. In practice, we propose meta-explanations to explain the Shapley values and make them more accessible and trustworthy to human users. On four synthetic and nine real-world MRFs, we demonstrate that GraphShapley generates sensible and practical explanations.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3340531.3411881 | CIKM '20: The 29th ACM International Conference on Information and Knowledge Management
Virtual Event
Ireland
October, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-6859-9 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yifei Liu | 1 | 0 | 0.34 |
| Chen Chao | 2 | 22 | 3.99 |
| Yazheng Liu | 3 | 0 | 0.34 |
| Xi Zhang | 4 | 1 | 2.06 |
| Sihong Xie | 5 | 483 | 33.80 |