Preprint
Jul 2026
Flow Matching with Missing Data
This work proposes Missing-Data Flow Matching, which treats the missing coordinates of training samples as latent variables and averages the flow matching loss over the values they could take, and places the method alongside strong classical and deep imputation baselines on real tabular data.
Fairoz Nower Khan, Nabuat Zaman Nahim, Peizhong Ju
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