3 papers
cs.HC2026
Reproducibility Beyond Artifacts: Interactional Support for Collaborative Machine Learning
Zhiwei Li, Carl Kesselman
Machine learning (ML) reproducibility is often framed as a problem of incomplete artifact recording. This framing leads to systems that prioritize capturing datasets, code, configu…
cs.LG2025
From Data to Decision: Data-Centric Infrastructure for Reproducible ML in Collaborative eScience
Zhiwei Li, Carl Kesselman, Tran Huy Nguyen +3
Reproducibility remains a central challenge in machine learning (ML), especially in collaborative eScience projects where teams iterate over data, features, and models. Current ML…
cs.LG2024
Deriva-ML: A Continuous FAIRness Approach to Reproducible Machine Learning Models
Zhiwei Li, Carl Kesselman, Mike D'Arch +2
Increasingly, artificial intelligence (AI) and machine learning (ML) are used in eScience applications [9]. While these approaches have great potential, the literature has shown th…