Related Experiment Video
Updated: Aug 13, 2026
13:51
Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
ICYM2I: The illusion of multimodal informativeness under missingness
Young Sang Choi1, Vincent Jeanselme1, Pierre Elias1,2
1Department of Biomedical Informatics, Columbia University, New York, NY, USA.
... International Conference on Learning Representations
|August 12, 2026
Summary
Multimodal learning in AI faces challenges when data is missing in new environments. This study introduces a new framework to accurately assess information gain from additional data modalities, even with missing data.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Data Science
Background:
- Multimodal learning leverages diverse data types for enhanced AI performance.
- Missing data modalities between training and deployment environments pose a significant challenge.
- Current methods may inaccurately estimate modality value due to unaddressed missingness patterns.
Purpose of the Study:
- To formalize the problem of differing missingness patterns in multimodal learning.
- To demonstrate the bias introduced by ignoring missing data processes.
- To introduce a framework for evaluating predictive performance and information gain under missingness.
Main Methods:
- Formalization of missingness in multimodal datasets.
- Demonstration of distribution shift induced by missingness.
- Introduction of the ICYM²I (In Case You Multimodal Missed It) framework.
- Application of inverse probability weighting for bias correction.
Main Results:
- Missingness pattern shifts between environments are common and impactful.
- Ignoring missingness leads to biased estimates of information gain.
- The ICYM²I framework effectively corrects for missingness bias.
- The proposed adjustment improves information gain estimation across various datasets.
Conclusions:
- Accounting for missing data is crucial for reliable multimodal AI.
- The ICYM²I framework provides a robust method for evaluating multimodal AI under missingness.
- This research highlights the need for careful consideration of data availability in real-world AI deployments.
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