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This paper proposes a unified misspecification-reduction viewpoint for non-stationary linear bandits with round-specific feasible decision sets, achieving optimal dynamic regret without the restrictive orthogonal-structure assumption.
This paper studies piecewise-stationary low-rank linear contextual bandits, proposes the SPSC algorithm that achieves dynamic regret scaling with the intrinsic rank instead of the ambient dimension, and characterizes the identification boundary for subspace recovery under scalar feedback.