SPIN Unprocessed July 7, 2026 ai_technology research
Dynamic Regret for Non-Stationary Linear Bandits via Misspecification Reductions
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arXiv:2607.02891v1 Announce Type: new Abstract: Many online decision-making problems involve both round-specific feasible actions and drifting reward models: eligible ad impressions, feasible prices, and available treatments can change over time, while user preferences, demand curves, and patient responses may evolve. Motivated by these applications, we study non-stationary linear bandits with round-specific feasible decision sets. Existing methods that obtain the optimal \(\widetilde O(T^{2/3}P
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