79 citations · 519 across the 58 of their papers we have counts for
9 papers · 1 filter
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…
On the Sample Complexity of Representation Learning in Multi-task Bandits with Global and Local structure
Alessio Russo, Alexandre Proutiere
We investigate the sample complexity of learning the optimal arm for multi-task bandit problems. Arms consist of two components: one that is shared across tasks (that we call repre…
Navigating to the Best Policy in Markov Decision Processes
Aymen Al Marjani, Aurélien Garivier, Alexandre Proutiere
We investigate the classical active pure exploration problem in Markov Decision Processes, where the agent sequentially selects actions and, from the resulting system trajectory, a…
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…
Adaptive Sampling for Best Policy Identification in Markov Decision Processes
Aymen Al Marjani, Alexandre Proutiere
We investigate the problem of best-policy identification in discounted Markov Decision Processes (MDPs) when the learner has access to a generative model. The objective is to devis…
Optimal Best-arm Identification in Linear Bandits
Yassir Jedra, Alexandre Proutiere
We study the problem of best-arm identification with fixed confidence in stochastic linear bandits. The objective is to identify the best arm with a given level of certainty while…
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…
Minimal Exploration in Structured Stochastic Bandits
Richard Combes, Stefan Magureanu, Alexandre Proutiere
This paper introduces and addresses a wide class of stochastic bandit problems where the function mapping the arm to the corresponding reward exhibits some known structural propert…