A novel online weighted aggregation mechanism for truthful preference feedback in mobile crowdsourcing achieves sublinear regret O(sqrt(T)) and truthfulness in a dynamic Bayesian game, with an extension for limited feedback per slot.
Online mixture of experts: No-regret learning for optimal collective decision-making,
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Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing
A novel online weighted aggregation mechanism for truthful preference feedback in mobile crowdsourcing achieves sublinear regret O(sqrt(T)) and truthfulness in a dynamic Bayesian game, with an extension for limited feedback per slot.