arXiv stat.ML
· Papers
Bandit PCA with Minimax Optimal Regret
arXiv:2607.10936v2 Announce Type: replace-cross Abstract: We study the bandit-feedback version of online principal component analysis (Bandit PCA): in each round $t = 1,dots,T$, the adversary selects a $d times d$ symmetric gain matrix $G_t$ with spectrum in $[0,1]$ and rank at most $r$; the learner simultaneously se