aai-institute / aai-institute/pyDVL
Paper repro: DUL
- Langage dominant
- Python
- Étoiles
- 146
- Forks
- 10
- Métriques de merge des PR
- Aucune PR mergée en 30 j
Description
_Wang, Tianhao, Yu Yang, and Ruoxi Jia. “Improving Cooperative Game Theory-Based Data Valuation via Data Utility Learning.” arXiv, 2022. https://doi.org/10.48550/arXiv.2107.06336._
- [x] #157
- [x] #138
- [ ] Run all experiments
Guide de contribution
Ouvrir le guide de contribution
Piste de recherche
The issue references a paper and two completed tasks (#157, #138). Start by reading the linked arXiv paper to understand the DUL method. Then examine the closed issues to see what implementations or experiments were already done. The remaining task is to run all experiments, which likely involves setting up experimental pipelines, possibly in an experiments/ directory, and ensuring reproducibility. Check for existing experiment scripts or configuration files in the repository.
Rédigé par le modèle d'indexation à partir du texte de l'issue.
Évaluation
- Stack technique
- python
- Domaine
- data, machine-learning
- Type d'issue
- Fonctionnalité
- Difficulté
- 5/5
- Temps estimé
- Plus d'une semaine
- Activité
- À l'abandon
- Clarté
- À clarifier
- Accessibilité débutants
- 10/100