Statistical Consequences of Dueling Bandits
Multi-Armed-Bandit frameworks have often been used by researchers to assess educational interventions, however, recent work has shown that it is more beneficial for a student to provide qualitative feedback through preference elicitation between different alternatives, making a dueling bandits framework more appropriate. In this paper, we explore the statistical quality of data under this framework by comparing traditional uniform sampling to a dueling bandit algorithm and find that dueling bandit algorithms perform well at cumulative regret minimisation, but lead to inflated Type-I error rates and reduced power under certain circumstances. Through these results we provide insight into the challenges and opportunities in using dueling bandit algorithms to run adaptive experiments.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Utility-based Dueling Bandits as a Partial Monitoring Game
Partial monitoring is a generic framework for sequential decision-making with incomplete feedback. It encompasses a wide class of problems such as dueling bandits, learning with expect advice, dynamic pricing, dark pools…
Decision MakingSequential Decision MakingKLUCB Approach to Copeland Bandits
Multi-armed bandit(MAB) problem is a reinforcement learning framework where an agent tries to maximise her profit by proper selection of actions through absolute feedback for each action. The dueling bandits problem is a…
Information RetrievalReinforcement LearningRetrievalThompson SamplingDouble Thompson Sampling for Dueling Bandits
In this paper, we propose a Double Thompson Sampling (D-TS) algorithm for dueling bandit problems. As indicated by its name, D-TS selects both the first and the second candidates according to Thompson Sampling. Specifica…
Thompson SamplingMulti-dueling Bandits with Dependent Arms
The dueling bandits problem is an online learning framework for learning from pairwise preference feedback, and is particularly well-suited for modeling settings that elicit subjective or implicit human feedback. In this…
Thompson SamplingFeel-Good Thompson Sampling for Contextual Dueling Bandits
Contextual dueling bandits, where a learner compares two options based on context and receives feedback indicating which was preferred, extends classic dueling bandits by incorporating contextual information for decision…
Decision MakingMulti-Armed BanditsThompson Sampling