DRIO adds worst-case Wasserstein regularization to time series imputation, yielding a tractable adversarial surrogate and alternating algorithm that improves robustness under missingness.
Gain: Missing data imputation using generative adversarial nets
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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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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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Efficacy Analysis in Clinical Trials: A Comprehensive Review of Statistical and Machine Learning Approaches
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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