Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
On Translations between ML Models for XAI Purposes
Authors: Alexis de Colnet, Pierre Marquis
IJCAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 19,985 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,730 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, the succinctness of various ML models is studied. To be more precise, the existence of polynomial-time and polynomial-space translations between representation languages for classifiers is investigated. ... We provide a complete map indicating for every pair of languages C, C whether or not a polynomial-time / polynomial-space translation exists from C to C . |
| Researcher Affiliation | Academia | Alexis de Colnet1 and Pierre Marquis2,3 1 Algorithms and Complexity Group, TU Wien, Vienna, Austria 2 Univ. Artois, CNRS, Centre de Recherche en Informatique de Lens (CRIL), F-62300 Lens, France 3Institut Universitaire de France |
| Pseudocode | No | The paper describes mathematical transformations and theoretical concepts but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve the use of datasets with train/validation/test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, hyperparameters, or training configurations. |