Reviews: Lifted Weighted Mini-Bucket

The paper proposes a lifted version of the weighted mini-bucket inference algorithm. Weighted mini-bucket is a variant of Variable Elimination that can trade off computational cost (e.g., achieving runtime sub-exponential in the treewidth) for accuracy. It essentially uses Holder inequality and variational approximations to represent "messages" that do not fit in the available memory budget. The main idea is to extend the approach to relational models (e.g., Markov Logic Networks) to take advantage of the additional structure, specifically, the fact that ground factors are produced from first-order templates and therefore share significant structure. The work appears to be solid, but it is difficult to parse.