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..
Delegation-Relegation for Boolean Matrix Factorization
Authors: Florent Avellaneda, Roger Villemaire
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, our experiments show that our approach outperforms existing exact BMF algorithms. In this paper, we present a new approach to simplifying the matrix to be factorized by reducing the number of 1-entries, which allows to directly recover a Boolean factorization of the original matrix from its simplified version. We conducted an evaluation of our methods, Simpli and Simpli , focusing on two key aspects: the degree of simplification they achieve and their effect on the time savings when performing factorizations on the simplified matrices using existing constraint-based BMF solvers. |
| Researcher Affiliation | Academia | Florent Avellaneda1, 2, Roger Villemaire1 1 Universit e du Qu ebec a Montr eal (UQAM), Montr eal, Canada 2 Centre de Recherche de l Institut Universitaire de G eriatrie de Montr eal, Montr eal, Canada EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Simpli OP; Algorithm 2: BMF through simplified matrix |
| Open Source Code | Yes | 1https://github.com/Florent Avellaneda/Delegation BMF |
| Open Datasets | Yes | We considered 31 classic datasets from the literature, namely: Audiology , Autism , Balance Scale , Brest Cancer , Car Evaluation , Chess , Contraceptive Method Choice , Dermatology , Firewall , Solar Flare , Heart Disease , Hepatitis , Iris , Lymphography , Mushroom , Nursery , Website Phishing , Soybean , Student Performance , Thoracic Surgery , Tic Tac-Toe Endgame , Primary Tumor , Voting Records , Wine , Zoo from UCI (Kelly, Longjohn, and Nottingham 2023), Americas-small , Apj , Customer from (Ene et al. 2008), DNA from (Myllykangas et al. 2006) and Paleo from (หZliobait e et al. 2023). |
| Dataset Splits | No | The paper describes experiments on Boolean Matrix Factorization, which operates on single matrices rather than using traditional machine learning train/validation/test splits. Therefore, such dataset splits are not explicitly provided for reproducing the experiment in this context. |
| Hardware Specification | Yes | Our algorithms were implemented in C++1, and we performed the experiments on an Intel Gold 6148 Skylake processor using a single thread and 32 Go of RAM. |
| Software Dependencies | No | The paper states that algorithms were implemented in C++ and mentions the use of existing BMF solvers (CG and Opti Block), but it does not specify version numbers for C++ compilers or any specific libraries used within the implementation, nor for the solvers themselves beyond their names. |
| Experiment Setup | Yes | We set a time limit of 3 hours and recorded the execution time and rank found for CG in Table 1 and for Opti Block in Table 2. |