7 citations · 19 across the 31 of their papers we have counts for
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cs.LG2023
Best-of-Both-Worlds Linear Contextual Bandits
Masahiro Kato, Shinji Ito
This study investigates the problem of -armed linear contextual bandits, an instance of the multi-armed bandit problem, under an adversarial corruption. At each round, a decisio…
cs.LG2023
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds
Taira Tsuchiya, Shinji Ito, Junya Honda
Adaptivity to the difficulties of a problem is a key property in sequential decision-making problems to broaden the applicability of algorithms. Follow-the-regularized-leader (FTRL…
cs.LG2023
Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive Regret Bounds
Shinji Ito, Kei Takemura
This paper proposes a linear bandit algorithm that is adaptive to environments at two different levels of hierarchy. At the higher level, the proposed algorithm adapts to a variety…