Sharp Regret Bounds and a Task-Covariance Correction for Spectral Representation Learning
This work derives alignment-dependent regret bounds, matching worst-case lower bounds for a flat leading spectrum, and bounds using the leading $2k$ directions with a spectral-tail term, and bound the imbalance from random preference patterns and from averaging independent tasks with an isotropic population covariance.