2023
DOI: 10.48550/arxiv.2302.04441
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Multi-task Representation Learning for Pure Exploration in Linear Bandits

Abstract: Despite the recent success of representation learning in sequential decision making, the study of the pure exploration scenario (i.e., identify the best option and minimize the sample complexity) is still limited. In this paper, we study multi-task representation learning for best arm identification in linear bandits (RepBAI-LB) and best policy identification in contextual linear bandits (RepBPI-CLB), two popular pure exploration settings with wide applications, e.g., clinical trials and web content optimizati… Show more

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