Introduces MO-ARM framework for training order-agnostic autoregressive models directly on incomplete data, showing implicit MCAR imputation in standard training and outperforming baselines on benchmarks.
Gain: Missing data imputation using generative adversarial nets
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DRIO adds worst-case Wasserstein regularization to time series imputation, yielding a tractable adversarial surrogate and alternating algorithm that improves robustness under missingness.
A review summarizing parametric, nonparametric, Bayesian, and machine learning methods for efficacy analysis in clinical trials and identifying gaps such as high-dimensional data and missingness.
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Order-Agnostic Autoregressive Modelling with Missing Data
Introduces MO-ARM framework for training order-agnostic autoregressive models directly on incomplete data, showing implicit MCAR imputation in standard training and outperforming baselines on benchmarks.
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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.