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By cleverly mining historical data for invariances, ISD-linUCB achieves superior regret in non-stationary bandits, even when the reward model changes rapidly.
Unlock domain generalization with unlabeled data by exploiting the structure of anti-causal relationships, where outcomes cause covariates.
