MissScore: High-Order Score Estimation in the Presence of Missing Data
Liu, Wenqin Hou, Haoze Gao, Erdun Huang, Biwei Ke, Qiuhong Bondell, Howard D. Gong, Mingming
Liu, Wenqin
Hou, Haoze
Gao, Erdun
Huang, Biwei
Ke, Qiuhong
Bondell, Howard D.
Gong, Mingming
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Score-based generative models are essential in various machine learning applications, with strong capabilities in generation quality. In particular, high-order derivatives (scores) of data density offer deep insights into data distributions, building on the proven effectiveness of first-order scores for modeling and generating synthetic data, unlocking new possibilities for applications. However, learning them typically requires complete data, which is often unavailable in domains such as healthcare and finance due to data corruption, acquisition constraints, or incomplete records. To tackle this challenge, we introduce MissScore, a novel framework for estimating high-order scores in the presence of missing data. We derive objective functions for estimating high-order scores under different missing data mechanisms and propose a new algorithm specifically designed to handle missing data effectively. Our empirical results demonstrate that MissScore accurately and efficiently learns the high-order scores from incomplete data and generates high-quality samples, resulting in strong performance across a range of downstream tasks.
Citation
W. Liu et al., “MissScore: High-Order Score Estimation in the Presence of Missing Data,” Oct. 06, 2025, PMLR. [Online]. Available: https://proceedings.mlr.press/v267/liu25aa.html
Source
Proceedings of Machine Learning Research
Conference
42nd International Conference on Machine Learning, ICML 2025
Keywords
Subjects
Source
42nd International Conference on Machine Learning, ICML 2025
Publisher
ML Research Press