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Found 3 papers, 1 papers with code
Optimal and Adaptive Non-Stationary Dueling Bandits Under a Generalized Borda Criterion
In dueling bandits, the learner receives preference feedback between arms, and the regret of an arm is defined in terms of its suboptimality to a winner arm.
When Can We Track Significant Preference Shifts in Dueling Bandits?
Specifically, we study the recent notion of significant shifts (Suk and Kpotufe, 2022), and ask whether one can design an adaptive algorithm for the dueling problem with $O(\sqrt{K\tilde{L}T})$ dynamic regret, where $\tilde{L}$ is the (unknown) number of significant shifts in preferences.
Tracking Most Significant Arm Switches in Bandits
In bandit with distribution shifts, one aims to automatically adapt to unknown changes in reward distribution, and restart exploration when necessary.
