5 citations · 18 across the 33 of their papers we have counts for
6 papers · 1 filter
Policy Testing in Markov Decision Processes
Kaito Ariu, Po-An Wang, Alexandre Proutiere +1
We study the policy testing problem in discounted Markov decision processes (MDPs) in the fixed-confidence setting under a generative model with static sampling. The goal is to dec…
On Universally Optimal Algorithms for A/B Testing
Po-An Wang, Kaito Ariu, Alexandre Proutiere
We study the problem of best-arm identification with fixed budget in stochastic multi-armed bandits with Bernoulli rewards. For the problem with two arms, also known as the A/B tes…
Optimal Best Arm Identification in Two-Armed Bandits with a Fixed Budget under a Small Gap
Masahiro Kato, Kaito Ariu, Masaaki Imaizumi +2
We consider fixed-budget best-arm identification in two-armed Gaussian bandit problems. One of the longstanding open questions is the existence of an optimal strategy under which t…
Regret in Online Recommendation Systems
Kaito Ariu, Narae Ryu, Se-Young Yun +1
This paper proposes a theoretical analysis of recommendation systems in an online setting, where items are sequentially recommended to users over time. In each round, a user, rando…
Thresholded Lasso Bandit
Kaito Ariu, Kenshi Abe, Alexandre Proutière
In this paper, we revisit the regret minimization problem in sparse stochastic contextual linear bandits, where feature vectors may be of large dimension , but where the reward…
Optimal Clustering from Noisy Binary Feedback
Kaito Ariu, Jungseul Ok, Alexandre Proutiere +1
We study the problem of clustering a set of items from binary user feedback. Such a problem arises in crowdsourcing platforms solving large-scale labeling tasks with minimal effort…