1 citations · 2 across the 5 of their papers we have counts for
5 papers
Agile Online Model Selection: Resolving Adaptation Lag via Safeguarded Large Learning Rates
Kei Takemura, Ryuta Matsuno, Keita Sakuma
Maintaining predictive accuracy in non-stationary environments requires online model selection to adapt autonomously to unknown distribution shifts. However, existing tuning-free a…
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…
Online Task Assignment Problems with Reusable Resources
Hanna Sumita, Shinji Ito, Kei Takemura +4
We study online task assignment problem with reusable resources, motivated by practical applications such as ridesharing, crowdsourcing and job hiring. In the problem, we are given…
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…
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…