SPSC recovers drifting low-rank subspaces from scalar rewards under known noise variance, bounded coupling, and full probe support, then achieves dynamic regret scaling as r sqrt(T) plus lower-order terms instead of d sqrt(T).
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Catching a Moving Subspace: Low-Rank Bandits Beyond Stationarity
SPSC recovers drifting low-rank subspaces from scalar rewards under known noise variance, bounded coupling, and full probe support, then achieves dynamic regret scaling as r sqrt(T) plus lower-order terms instead of d sqrt(T).