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
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
citing papers explorer
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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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Corruptions of Supervised Learning Problems: Typology and Mitigations
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.
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MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling
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.
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DynoSys: A Dynamic Systems Framework for Multimodal Integration of Genetic, Environmental, and Neurobiological Signals
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.
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Bayesian Joint Modelling of Longitudinal Creatinine Trajectories in Children with Auto-Immune Disorders to Predict Paediatric Kidney Disease Risk in a Single Centre Study
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.
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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.