Sharp Regret Bounds and a Task-Covariance Correction for Spectral Representation Learning

Spectral features can remain optimal under strongly uneven task preferences when they retain the directions most useful to the tasks. In a local-task model, expected probing gain depends on the task prior only through its covariance $Λ$: with $B$ recording dependence between views, $k$ spectral feat