Researchers have introduced a novel framework for achieving meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback. This new approach extends the Shapley value concept to a K-Shapley value, which quantifies an agent's marginal contribution within a limited set size. The proposed K-SVFair-FBF algorithm adaptively estimates this K-Shapley value, demonstrating improved fairness and performance on datasets related to federated learning and social influence maximization. AI
影响 Introduces a new fairness metric and algorithm for bandit problems, potentially improving resource allocation in complex systems.
排序理由 Academic paper introducing a new algorithmic framework and theoretical results.
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