Abstract:We analyze an online learning problem that arises in crowdsourcing systems for users facing crowdsourced data: a user at each discrete time step t can choose K out of a total of N options (bandits), and receives randomly generated rewards dependent on user-specific and option-specific statistics unknown to the user. Each user aims to maximize her expected total rewards over a certain time horizon through a sequence of exploration and exploitation steps. Different from the typical regret/bandit learning setting… Show more
“…Our future direction aims to dig deeper in both directions of crowd learning [3] as well as crowd teaching. From the learning perspective, we would like to explore the performance of using embedded features combined with the crowdsourced labels.…”
“…Our future direction aims to dig deeper in both directions of crowd learning [3] as well as crowd teaching. From the learning perspective, we would like to explore the performance of using embedded features combined with the crowdsourced labels.…”
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