48 citations · 48 across the 4 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2020
Approximation Theory Based Methods for RKHS Bandits
Sho Takemori, Masahiro Sato
The RKHS bandit problem (also called kernelized multi-armed bandit problem) is an online optimization problem of non-linear functions with noisy feedback. Although the problem has…
cs.LG2020★ 48 cited
Unbiased Learning for the Causal Effect of Recommendation
Masahiro Sato, Sho Takemori, Janmajay Singh +1
Increasing users' positive interactions, such as purchases or clicks, is an important objective of recommender systems. Recommenders typically aim to select items that users will i…
cs.LG2020
Submodular Bandit Problem Under Multiple Constraints
Sho Takemori, Masahiro Sato, Takashi Sonoda +2
The linear submodular bandit problem was proposed to simultaneously address diversified retrieval and online learning in a recommender system. If there is no uncertainty, this prob…