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].
Structure Learning with Adaptive Random Neighborhood Informed MCMC
Authors: Xitong Liang, Alberto Caron, Samuel Livingstone, Jim Griffin
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | After introducing the technical novelties in PARNI-DAG, we empirically demonstrate its mixing efficiency and accuracy in learning DAG structures on a variety of experiments.1 |
| Researcher Affiliation | Academia | Alberto Caron The Alan Turing Institute London, UK EMAIL Liang Department of Statistical Sciences University College London London, UK EMAIL Livingstone Department of Statistical Sciences University College London London, UK EMAIL Griffin Department of Statistical Sciences University College London London, UK EMAIL |
| Pseudocode | Yes | The full details and algorithmic pseudo code of PARNI-DAG are also provided in the supplementary material (Appendix D). |
| Open Source Code | Yes | 1Code to implement the PARNI-DAG proposal and replicate the experimental sections is available at https://github.com/Xitong Liang/The-PARNI-scheme/tree/main/Structure-Learning. |
| Open Datasets | Yes | We first consider the real-world protein-signalling dataset [Sachs et al., 2005], found also in Cundy et al. [2021], to test PARNI-DAG s mixing. |
| Dataset Splits | No | The paper mentions generating N=100 observations but does not provide specific details on train/validation/test splits, percentages, or explicit sample counts for the experimental setup. |
| Hardware Specification | Yes | Using Intel i7 2.80 GHz processor |
| Software Dependencies | No | The paper mentions 'R package bnlearn' but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | For ADR and PARNI-DAG, we use prior parameters g = 10 and h = 1/11. |