K-SVFair-FBF uses the new K-Shapley value to achieve meritocratic fairness with O(T^{3/4}) regret in budgeted combinatorial bandits under full-bandit feedback.
Thompson sampling for combinatorial semi-bandits with sleeping arms and long- term fairness constraints.arXiv preprint arXiv:2005.06725
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Sequential explore-exploit algorithms for assigning tasks to capacity-constrained agents demonstrate performance gains over non-contextual baselines on tabular, image, and text tasks with both LLMs and humans.
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Meritocratic Fairness in Budgeted Combinatorial Multi-armed Bandits via Shapley Values
K-SVFair-FBF uses the new K-Shapley value to achieve meritocratic fairness with O(T^{3/4}) regret in budgeted combinatorial bandits under full-bandit feedback.
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Learning to Assign Prediction Tasks to Agents with Capacity Constraints
Sequential explore-exploit algorithms for assigning tasks to capacity-constrained agents demonstrate performance gains over non-contextual baselines on tabular, image, and text tasks with both LLMs and humans.