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.
John Wiley & Sons
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The paper introduces a Markov kernel framework for exhaustively classifying corruptions in supervised learning and derives loss corrections for label, attribute, and joint cases by comparing clean and corrupted Bayes risks.
MILM fine-tunes LLMs on XML-encoded multimodal irregular time series via a two-stage process that exploits informative sampling patterns to achieve top performance on EHR classification datasets.
DynoSys offers a unified dynamic systems model integrating genetic, environmental, and neurobiological signals to analyze longitudinal behavioral phenotypes in adolescents via harmonized representations and survival or state-space modeling.
Joint Bayesian models link longitudinal creatinine trajectories to time-to-event kidney disease risk in pediatric autoimmune patients and enable dynamic risk predictions based on observed data.
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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