Fast Lifted MAP Inference via Partitioning
Authors: Somdeb Sarkhel, Parag Singla, Vibhav G. Gogate
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on several real-world datasets clearly show that our new algorithm is superior to previous approaches and often finds useful symmetries in the search space that existing lifted inference rules are unable to detect. We implemented our algorithm on top of the lifted MAP algorithm of Sarkhel et al. [18], which reduces lifted MAP inference to an integer polynomial program (IPP). We performed two sets of experiments. |
| Researcher Affiliation | Academia | Somdeb Sarkhel The University of Texas at Dallas Parag Singla I.I.T. Delhi Vibhav Gogate The University of Texas at Dallas |
| Pseudocode | Yes | Algorithm 1 LMAP(MLN M) Algorithm 2 Constrained-Ground (MLN M, Size k and domain equivalence class U) Algorithm 3 Partition-Ground (MLN M, Size k and domain equivalence class U) Algorithm 4 Refine-MAP(MLN M) |
| Open Source Code | No | The paper mentions "Alchemy (ALY) [11]" and "Tuffy (TUFFY) [15]" as "open source software packages" that they compare against, and provides a URL for Alchemy. However, it does not state that the code for *their own* proposed algorithm (P-IPP) is open-source or provide a link to its repository. |
| Open Datasets | Yes | We used following five MLNs in our experimental study: (1) An MLN which we call Equivalence that consists of following three formulas...; (2) The Student MLN from [18, 19]...; (3) The Relationship MLN from [18]...; (4) Web KB MLN [11] from the Alchemy web page...; and (5) Citation Information-Extraction (IE) MLN from the Alchemy web page [11].... [11] S. Kok, M. Sumner, M. Richardson, P. Singla, H. Poon, D. Lowd, J. Wang, and P. Domingos. The Alchemy System for Statistical Relational AI. Technical report, Department of Computer Science and Engineering, University of Washington, Seattle, WA, 2008. http://alchemy.cs.washington.edu. |
| Dataset Splits | No | The paper does not provide specific details on how datasets were split into training, validation, or test sets (e.g., percentages, sample counts, or explicit standard splits). |
| Hardware Specification | Yes | All of our experiments were run on a third generation i7 quad-core machine having 8GB RAM. |
| Software Dependencies | No | The paper mentions using "Gurobi [8]" as an Integer Linear Programming solver. While it cites the Gurobi Reference Manual, the text itself does not provide a specific version number for Gurobi or any other key software component. |
| Experiment Setup | No | The paper describes the algorithms and their modifications but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rates, batch sizes), optimization settings, or initialization procedures used during the experiments. |