2 citations · 3 across the 3 of their papers we have counts for
7 papers
Learning with Posterior Sampling for Revenue Management under Time-varying Demand
Kazuma Shimizu, Junya Honda, Shinji Ito +1
This paper discusses the revenue management (RM) problem to maximize revenue by pricing items or services. One challenge in this problem is that the demand distribution is unknown…
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…
On Optimal Robustness to Adversarial Corruption in Online Decision Problems
Shinji Ito
This paper considers two fundamental sequential decision-making problems: the problem of prediction with expert advice and the multi-armed bandit problem. We focus on stochastic re…
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…
Causal Bandits with Propagating Inference
Akihiro Yabe, Daisuke Hatano, Hanna Sumita +4
Bandit is a framework for designing sequential experiments. In each experiment, a learner selects an arm and obtains an observation corresponding to . Theore…