Pareto regret in multi-objective bandits matches the single-objective case by scaling inversely with the largest objective-wise gap g†, independent of dimension d, via a new top-two races and uncertainty-greedy algorithm with matching bounds.
Direct preference-based evolutionary multi-objective optimization with dueling bandits
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Are Stochastic Multi-objective Bandits Harder than Single-objective Bandits?
Pareto regret in multi-objective bandits matches the single-objective case by scaling inversely with the largest objective-wise gap g†, independent of dimension d, via a new top-two races and uncertainty-greedy algorithm with matching bounds.