On the Computation of Necessary and Sufficient Explanations
Authors: Adnan Darwiche, Chunxi Ji5582-5591
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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 darwiche@cs.ucla.edu, jich@cs.ucla.edu |
| 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). |