NIPS Proceedingsβ

A KL-LUCB algorithm for Large-Scale Crowdsourcing

Part of: Advances in Neural Information Processing Systems 30 (NIPS 2017)


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Conference Event Type: Poster


This paper focuses on best-arm identification in multi-armed bandits with bounded rewards. We develop an algorithm that is a fusion of lil-UCB and KL-LUCB, offering the best qualities of the two algorithms in one method. This is achieved by proving a novel anytime confidence bound for the mean of bounded distributions, which is the analogue of the LIL-type bounds recently developed for sub-Gaussian distributions. We corroborate our theoretical results with numerical experiments based on the New Yorker Cartoon Caption Contest.