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..
A General Theoretical Framework for Learning Smallest Interpretable Models
Authors: Sebastian Ordyniak, Giacomo Paesani, Mateusz Rychlicki, Stefan Szeider
AAAI 2024 | Venue PDF | 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. |