Introduces symmetry-aware convex shrinkage for high-dimensional covariance estimation by selecting a symmetry group via held-out negative log-likelihood and proving regret bounds plus dominance over Ledoit-Wolf under a match condition.
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Proposes an inferential framework to test differences in categorical Gini correlations for predictor importance in classification, establishing asymptotic normality and consistency while accommodating unequal dimensions and dependence.
A pathway-constrained autoencoder extended to multi-omics integration improves breast cancer stratification and provides interpretable pathway activity scores.
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Symmetry-Aware Convex Shrinkage for High-Dimensional Covariance Estimation
Introduces symmetry-aware convex shrinkage for high-dimensional covariance estimation by selecting a symmetry group via held-out negative log-likelihood and proving regret bounds plus dominance over Ledoit-Wolf under a match condition.
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Comparing Two Categorical Gini Correlations with Applications to Classification Problems
Proposes an inferential framework to test differences in categorical Gini correlations for predictor importance in classification, establishing asymptotic normality and consistency while accommodating unequal dimensions and dependence.
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Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer
A pathway-constrained autoencoder extended to multi-omics integration improves breast cancer stratification and provides interpretable pathway activity scores.
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