Public articles linked to the same research event.
arXiv The authors introduce Hierarchical Spectral Shrinkage (HSS), an empirical-Bayes framework that shrinks each task's leading spectral directions toward a data-adaptively learned common basis with shrinkage varying across tasks and spectral components, yielding the posterior mean of singular vectors in a surrogate Bayesian regression; across synthetic experiments and three immune-cell gene-expression datasets (sample sizes 156, 628, 146), HSS improves estimation accuracy and out-of-sample performance over separate, fully pooled, and shared-subspace approaches.
The authors introduce Hierarchical Spectral Shrinkage (HSS), an empirical-Bayes framework that shrinks each task's leading spectral directions toward a data-adaptively learned common basis with shrinkage varying across tasks and spectral components, yielding the posterior mean of singular vectors in a surrogate Bayesian regression; across synthetic experiments and three immune-cell gene-expression datasets (sample sizes 156, 628, 146), HSS improves estimation accuracy and out-of-sample performance over separate, fully pooled, and shared-subspace approaches.
The authors introduce Hierarchical Spectral Shrinkage (HSS), an empirical-Bayes framework that shrinks each task's leading spectral directions toward a data-adaptively learned common basis with shrinkage varying across tasks and spectral components, yielding the posterior mean of singular vectors in a surrogate Bayesian regression; across synthetic experiments and three immune-cell gene-expression datasets (sample sizes 156, 628, 146), HSS improves estimation accuracy and out-of-sample performance over separate, fully pooled, and shared-subspace approaches.
The authors introduce Hierarchical Spectral Shrinkage (HSS), an empirical-Bayes framework that shrinks each task's leading spectral directions toward a data-adaptively learned common basis with shrinkage varying across tasks and spectral components, yielding the posterior mean of singular vectors in a surrogate Bayesian regression; across synthetic experiments and three immune-cell gene-expression datasets (sample sizes 156, 628, 146), HSS improves estimation accuracy and out-of-sample performance over separate, fully pooled, and shared-subspace approaches.