Aggregation distorts parametric behavioral curve peaks by factors of 3-5x via Simpson's paradox and survival bias, shown by individual vs. aggregate comparisons on Goodreads and Amazon datasets with a negative control.
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HPPCA is a hierarchical extension of PPCA that uses Gaussian processes to model within-subject dynamics in longitudinal data, outperforming standard PPCA and functional PCA in imputation under missingness and misspecification.
A Gaussian mixture MIL framework with partially subsampled instances improves metastasis prediction accuracy on breast cancer whole-slide images over prior methods.
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Simpson's Paradox in Behavioral Curves: How Aggregation Distorts Parametric Models of User Dynamics
Aggregation distorts parametric behavioral curve peaks by factors of 3-5x via Simpson's paradox and survival bias, shown by individual vs. aggregate comparisons on Goodreads and Amazon datasets with a negative control.
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Hierarchical Probabilistic Principal Component Analysis of Longitudinal Data
HPPCA is a hierarchical extension of PPCA that uses Gaussian processes to model within-subject dynamics in longitudinal data, outperforming standard PPCA and functional PCA in imputation under missingness and misspecification.
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Detecting Breast Carcinoma Metastasis on Whole-Slide Images by Partially Subsampled Multiple Instance Learning
A Gaussian mixture MIL framework with partially subsampled instances improves metastasis prediction accuracy on breast cancer whole-slide images over prior methods.