Foundations of Symbolic Languages for Model Interpretability
Authors: Marcelo Arenas, Daniel Báez, Pablo Barceló, Jorge Pérez, Bernardo Subercaseaux
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also present a prototype implementation of FOIL wrapped in a high-level declarative language, and perform experiments showing that such a language can be used in practice. |
| Researcher Affiliation | Academia | Marcelo Arenas1,4, Daniel Baez3, Pablo Barceló2,4, Jorge Pérez3,4, Bernardo Subercaseaux4,5 1 Department of Computer Science, PUC-Chile 2 Institute for Mathematical and Computational Engineering, PUC-Chile 3 Department of Computer Science, Universidad de Chile 4 Millennium Institute for Foundational Research on Data, Chile 5 Carnegie Mellon University, USA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | A detailed exposition along with our implementation and a set of real examples can be found in the supplementary material. |
| Open Datasets | Yes | We tested a set of 20 handcrafted queries over decision trees with up to 400 leaves trained for the Student Performance Data Set [29], which combines Boolean and numerical features. |
| Dataset Splits | No | The paper mentions training models on 'random input data' and the 'Student Performance Data Set [29]', but does not specify any training, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | Yes | All experiments where run on a personal computer with a 2.48GHz Intel N3060 processor and 2GB RAM. The exact details of the machine are presented in the supplementary material. |
| Software Dependencies | No | The paper mentions 'Scikit-learn [30] library' but does not provide specific version numbers for it or any other key software components used in the experiments. |
| Experiment Setup | Yes | We tested the efficiency of our implementation varying three different parameters: the number of input features, the number of leaves of the decision tree, and the size of the input queries. We created a set of random queries with 1 to 4 quantified variables, and a varying number of operators (60 different queries). |