1 citations · 2 across the 5 of their papers we have counts for
Showing stat.MLShow all
2 papers · 1 filter
stat.ML2021
Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions
Kei Takemura, Shinji Ito, Daisuke Hatano +4
The contextual combinatorial semi-bandit problem with linear payoff functions is a decision-making problem in which a learner chooses a set of arms with the feature vectors in each…
stat.ML2019★ 1 cited
An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits
Kei Takemura, Shinji Ito
Combinatorial linear semi-bandits (CLS) are widely applicable frameworks of sequential decision-making, in which a learner chooses a subset of arms from a given set of arms associa…