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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
On the Computation of Necessary and Sufficient Explanations
Authors: Adnan Darwiche, Chunxi Ji5582-5591
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Table 1: Evaluating Algorithms 1 & 2. Times in secs. First ten entries are decision trees. Last five entries are decision graphs. |
| Researcher Affiliation | Academia | Computer Science Department University of California, Los Angeles EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Shortest Necessary Reasons (SNRs); Algorithm 2: Shortest Sufficient Reasons (SSRs) |
| Open Source Code | No | The paper does not provide a specific link or an explicit statement about the public release of its source code. |
| Open Datasets | Yes | Table 1 depicts an empirical evaluation on decision trees learned from Open ML datasets (Vanschoren et al. 2013)... |
| Dataset Splits | No | The paper states, 'Each dataset was split using WEKA into training (85%) and testing (15%) data,' but it does not mention a validation split. |
| Hardware Specification | Yes | We used a Python implementation on a dual Intel(R) Xeon E5-2670 CPUs running at 2.60GHz and 256GB RAM. |
| Software Dependencies | No | The paper mentions using 'WEKA s J48 classifier' and 'python-weka-wrapper3 available at pypi.org' and a 'Python implementation' but does not specify exact version numbers for these software dependencies. |
| Experiment Setup | No | The paper mentions using 'WEKA s J48 classifier with default settings' for learning decision trees, but it does not provide specific hyperparameters or detailed training configurations for its own proposed algorithms (SNR and SSR). |