A General Theoretical Framework for Learning Smallest Interpretable Models
Authors: Sebastian Ordyniak, Giacomo Paesani, Mateusz Rychlicki, Stefan Szeider
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop a general algorithmic framework that allows us to obtain fixed-parameter tractability for computing smallest symbolic models that represent given data. |
| Researcher Affiliation | Academia | 1University of Leeds, Leeds, UK, 2TU Wien, Vienna, Austria |
| Pseudocode | Yes | Algorithm 1: Generic Algorithm for finding a minimum model of size at most s for any strongly extendable modeltype T. Algorithm 2: Generic Algorithm for finding a minimum model of size at most s for any extendable model-type T. Algorithm 3: Algorithm for finding a full set of strict extensions for DLs. |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is a theoretical work and does not use or refer to specific datasets for empirical evaluation. |
| Dataset Splits | No | The paper is a theoretical work and does not describe any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper describes a theoretical framework and algorithms, and therefore does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |