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].
On the Kernelization of Global Constraints
Authors: Clément Carbonnel, Emmanuel Hebrard
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce novel loss-less kernelization variants that are tailored for constraint propagation. We showcase the theoretical interest of our ideas on two constraints, VERTEXCOVER and EDGEDOMINATINGSET. |
| Researcher Affiliation | Academia | Cl ement Carbonnel University of Oxford EMAIL Emmanuel Hebrard CNRS, LAAS-CNRS, Universit e de Toulouse EMAIL |
| Pseudocode | Yes | Algorithm 1: z Crown(G = (V, E), k, z) |
| Open Source Code | No | No statement explicitly providing a link to source code or stating its public availability for the described methodology was found. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and proofs for constraint satisfaction problems (VERTEX COVER, EDGE DOMINATING SET). It does not involve empirical studies with specific datasets, and therefore no mention of publicly available training datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments. Therefore, no hardware specifications were mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments involving specific experimental setups or hyperparameters. |