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An optimal algorithm for the Thresholding Bandit Problem
We study a specific combinatorial pure exploration stochastic bandit pro...
05/27/2016 ∙ by Andrea Locatelli, et al. ∙
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Multitask Bandit Learning through Heterogeneous Feedback Aggregation
In many real-world applications, multiple agents seek to learn how to pe...
10/29/2020 ∙ by Zhi Wang, et al. ∙
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Polynomial-time Algorithms for Combinatorial Pure Exploration with Full-bandit Feedback
We study the problem of stochastic combinatorial pure exploration (CPE),...
02/27/2019 ∙ by Yuko Kuroki, et al. ∙
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Maximin Action Identification: A New Bandit Framework for Games
We study an original problem of pure exploration in a strategic bandit m...
02/15/2016 ∙ by Aurélien Garivier, et al. ∙
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Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation
Combinatorial optimization is one of the fundamental research fields tha...
12/31/2020 ∙ by Yuko Kuroki, et al. ∙
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Non-Asymptotic Pure Exploration by Solving Games
Pure exploration (aka active testing) is the fundamental task of sequent...
06/25/2019 ∙ by Rémy Degenne, et al. ∙
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Volumetric Spanners: an Efficient Exploration Basis for Learning
Numerous machine learning problems require an exploration basis - a mech...
12/21/2013 ∙ by Elad Hazan, et al. ∙
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Instance-Sensitive Algorithms for Pure Exploration in Multinomial Logit Bandit
12/02/2020 ∙ by Nikolai Karpov, et al. ∙
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Motivated by real-world applications such as fast fashion retailing and online advertising, the Multinomial Logit Bandit (MNL-bandit) is a popular model in online learning and operations research, and has attracted much attention in the past decade. However, it is a bit surprising that pure exploration, a basic problem in bandit theory, has not been well studied in MNL-bandit so far. In this paper we give efficient algorithms for pure exploration in MNL-bandit. Our algorithms achieve instance-sensitive pull complexities. We also complement the upper bounds by an almost matching lower bound.
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