5 citations · 7 across the 5 of their papers we have counts for
5 papers
New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem
Koji Ichikawa, Shinji Ito, Daisuke Hatano +4
We consider the sparse contextual bandit problem where arm feature affects reward through the inner product of sparse parameters. Recent studies have developed sparsity-agnostic al…
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…
Non-zero-sum Stackelberg Budget Allocation Game for Computational Advertising
Daisuke Hatano, Yuko Kuroki, Yasushi Kawase +3
Computational advertising has been studied to design efficient marketing strategies that maximize the number of acquired customers. In an increased competitive market, however, a m…
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…