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A fully adaptive algorithm for pure exploration in linear bandits. · Study

WorkA fully adaptive algorithm for pure exploration in linear bandits.

Liyuan Xu2018

Work2018

A fully adaptive algorithm for pure exploration in linear bandits.

Liyuan Xu · Junya Honda · Masashi Sugiyama

33 citations·0/0 files stored·International Conference on Artificial Intelligence and Statistics

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Advanced Bandit Algorithms Research Reinforcement Learning in Robotics Optimization and Search Problems

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References

1952Some aspects of the sequential design of experiments 2011Improved Algorithms for Linear Stochastic Bandits 2009Pure Exploration in Multi-armed Bandits Problems 2014On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning 1999Associative Reinforcement Learning using Linear Probabilistic Concepts

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2014Best-Arm Identification in Linear Banditspreprint · 4 shared references 2012Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimizationpreprint · 3 shared references 2014From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planningarticle · 3 shared references 2015Optimally Confident UCB: Improved Regret for Finite-Armed Banditspreprint · 3 shared references 2011X -Armed Banditsarticle · 2 shared references 2013lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Banditspreprint · 2 shared references

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Year2018

Typeother

Published2018-03-31

VenueInternational Conference on Artificial Intelligence and Statistics

Languageen

Citations33

Files0 public / 0

ShelfAdvanced Bandit Algorithms Research

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openalex_workW2964063414

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