The work establishes a regret lower bound of Ω(T^{2/3} min(K,D)^{1/3}) for fair multi-user dueling bandits with heterogeneous Condorcet winners and gives algorithms achieving matching upper bounds up to logs.
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2 Pith papers cite this work. Polarity classification is still indexing.
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NASH decomposes the validation utility into Shapley-informative component functions and aggregates them non-linearly to make Data Shapley-based data selection consistently effective.
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Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare
The work establishes a regret lower bound of Ω(T^{2/3} min(K,D)^{1/3}) for fair multi-user dueling bandits with heterogeneous Condorcet winners and gives algorithms achieving matching upper bounds up to logs.
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Is Data Shapley Not Better than Random in Data Selection? Ask NASH
NASH decomposes the validation utility into Shapley-informative component functions and aggregates them non-linearly to make Data Shapley-based data selection consistently effective.