DRIO adds worst-case Wasserstein regularization to time series imputation, yielding a tractable adversarial surrogate and alternating algorithm that improves robustness under missingness.
not-miwae: Deep generative modelling with missing not at random data
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
MissBGM jointly models data generation and missingness in a Bayesian neural generative framework to produce consistent imputations with principled posterior uncertainty.
A conditional diffusion model trained on partitioned incomplete samples for physical dynamics achieves asymptotic convergence to the true generative process under mild conditions and outperforms baselines in imputation.
PRDIM is a diffusion model using a pattern recognizer to impute MNAR missing data by maximizing joint likelihood of observed values and missing mask via EM.
citing papers explorer
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Multivariate Time Series Data Imputation via Distributionally Robust Regularization
DRIO adds worst-case Wasserstein regularization to time series imputation, yielding a tractable adversarial surrogate and alternating algorithm that improves robustness under missingness.
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Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling
MissBGM jointly models data generation and missingness in a Bayesian neural generative framework to produce consistent imputations with principled posterior uncertainty.
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Incomplete Data, Complete Dynamics: A Diffusion Approach
A conditional diffusion model trained on partitioned incomplete samples for physical dynamics achieves asymptotic convergence to the true generative process under mild conditions and outperforms baselines in imputation.
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Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Random
PRDIM is a diffusion model using a pattern recognizer to impute MNAR missing data by maximizing joint likelihood of observed values and missing mask via EM.