[2603.21244] Amortized Variational Inference for Logistic Regression with Missing Covariates

Computer Science > Machine Learning arXiv:2603.21244 (cs) [Submitted on 22 Mar 2026] Title:Amortized Variational Inference for Logistic Regression with Missing Covariates Authors:M. Cherifi, Aude Sportisse, Xujia Zhu, Mohammed Nabil El Korso, A. Mesloub View a PDF of the paper titled Amortized Variational Inference for Logistic Regression with Missing Covariates, by M. Cherifi and 4 other authors View PDF HTML (experimental) Abstract: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, trained jointly by maximizing the evidence lower bound. Unlike competing methods, AV-LR performs inference directly in the space of missing data without additional latent variables, using a single inference network and a linear layer that jointly estimate regression parameters and the missingness mechanism. AV-LR achieves estimation accuracy comparable to or better ...