RAID algorithm achieves asymptotically minimal regret in incentive design under information asymmetry via a switching policy and a strongly consistent least-squares type estimator that relaxes persistence-of-excitation assumptions.
Optimal contract design for efficient federated learning with multi-dimensional private information,
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Adaptive Incentive Design with Regret Minimization
RAID algorithm achieves asymptotically minimal regret in incentive design under information asymmetry via a switching policy and a strongly consistent least-squares type estimator that relaxes persistence-of-excitation assumptions.