Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Lifting Relational MAP-LPs Using Cluster Signatures
Authors: Udi Apsel, Kristian Kersting, Martin Mladenov
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our new approach with several empirical results, putting special emphasis on the challenging class of transitive relational models. From the empirical results, we highlight several lifting of the SA hierarchy up to level 6, in non-trivial models. |
| Researcher Affiliation | Academia | Udi Apsel Computer Science Department Ben Gurion University of The Negev, Israel EMAIL Kristian Kersting and Martin Mladenov Computer Science Department TU Dortmund University, Germany EMAIL |
| Pseudocode | Yes | Algorithm 1: GENERATECOMPACTMAPLPk |
| Open Source Code | No | The paper mentions using the 'nauty (Mc Kay and Piperno 2014) software package' but does not state that the code for their own methodology is open-source or provide a link. |
| Open Datasets | No | The paper defines a 'transitive model' using a parfactor and mentions setting 'table entries were set at random', indicating synthetic data generation rather than the use of a publicly accessible dataset with explicit access information. |
| Dataset Splits | No | The paper does not specify dataset splits (e.g., train/validation/test percentages or counts) or reference standard predefined splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions using the 'nauty (Mc Kay and Piperno 2014) software package' and 'the GNU Linear Programming Kit (GLPK) simplex solver,' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We follow Algorithm 1, as follows. (1) Canonical clusters are obtained by generating all non-isomorphic instances of directed graphs with up to k edges, using the nauty (Mc Kay and Piperno 2014) software package, where all graphs assume a canonically labeled form. (2) A canonical cluster of size 3 consisting of nodes u, v, w and edges (u, v), (u, w), (v, w), is matched with the parfactor, and a linear expression involving its respective µ variables is added to the objective. (3) For each canonical cluster of size d, we obtain subset clusters of size d 1 for which local constraints are added, by removing edges (one each time) from the d size cluster s graph and obtaining a matching canonical labeling (nauty). |