Minimal Data Cleaning for Model Training by MinPrep
Published in PVLDB 2026 (to appear), 2026
Recommended citation: Zhen, C., Prayoga, Aryal, N., Termehchy, A., & Aghasi, A. (2026). Minimal Data Cleaning for Model Training by MinPrep. PVLDB, to appear. https://research.engr.oregonstate.edu/idea/sites/research.engr.oregonstate.edu.idea/files/vldb_demo_minprep.pdf
Minimal Data Cleaning for Model Training by MinPrep
Overview
MinPrep is a system for minimal data cleaning before supervised model training. Instead of asking users to clean all missing or dirty values, it helps identify the cleaning actions that are most relevant to downstream model quality.
The accepted PVLDB 2026 demo paper presents MinPrep as a practical system for targeted data cleaning in supervised learning workflows.
Authors
Cheng Zhen, Prayoga, Nischal Aryal, Arash Termehchy, and Alireza Aghasi.