Statistical Relational Learning Group
Tractable Marginal Inference for Hinge-Loss Markov Random Fields
| Title | Tractable Marginal Inference for Hinge-Loss Markov Random Fields |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Embar, V, Srinivasan, S, Getoor, L |
| Conference Name | ICML Workshop on TPM |
| Date Published | 06/2019 |
| Abstract | Hinge-loss Markov random fields (HL-MRFs) are a class of undirected graphical models that has been successfully applied to model richly structured data. HL-MRFs are defined over a set of continuous random variables in the range [0,1], which makes computing the MAP convex. However, computation of marginal distributions remain intractable. In this paper, we introduce a novel sampling-based algorithm to compute marginal distributions. We define the notion of association blocks, which help identify islands of high probability, and propose a novel approach to sample from these regions. We validate our approach by estimating both average precision and various properties of a social network. We show that the proposed approach outperforms MAP estimates in both average precision and the accuracy of the properties by 20% and 40% respectively on the large social network. |