Amortized Variational Inference for Logistic Regression with Missing Covariates
Missing covariate data pose a significant challenge to statistical inference and machine learning, particularly for classification tasks like logistic regression. Classical iterative approaches (EM, multiple imputation) are often computationally intensive, sensitive to high missingness rates, and limited in uncertainty propagation. Recent deep generative models based on VAEs show promise but rely on complex latent representations. We propose Amortized Variational Inference for Logistic Regression (AV-LR), a unified end-to-end framework for binary logistic regression with missing covariates. AV-LR integrates a probabilistic generative model with a simple amortized inference network, traine...
| Authors | M. Cherifi, Aude Sportisse, Xujia Zhu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 March 2026 |
| Last updated at source | 22 March 2026 |
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