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20152026
most citedOptimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays

70 citations · 161 across the 37 of their papers we have counts for

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Showing 2016Show all

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stat.ML2016★ 8 cited

Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm

Junpei Komiyama, Junya Honda, Hiroshi Nakagawa

We study the K-armed dueling bandit problem, a variation of the standard stochastic bandit problem where the feedback is limited to relative comparisons of a pair of arms. The hard…