Covariance Adaptive Best Arm Identification - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2023

Covariance Adaptive Best Arm Identification


We consider the problem of best arm identification in the multi-armed bandit model, under fixed confidence. Given a confidence input δ, the goal is to identify the arm with the highest mean reward with a probability of at least 1 − δ, while minimizing the number of arm pulls. While the literature provides solutions to this problem under the assumption of independent arms distributions, we propose a more flexible scenario where arms can be dependent and rewards can be sampled simultaneously. This framework allows the learner to estimate the covariance among the arms distributions, enabling a more efficient identification of the best arm. The relaxed setting we propose is relevant in various applications, such as clinical trials, where similarities between patients or drugs suggest underlying correlations in the outcomes. We introduce new algorithms that adapt to the unknown covariance of the arms and demonstrate through theoretical guarantees that substantial improvement can be achieved over the standard setting. Additionally, we provide new lower bounds for the relaxed setting and present numerical simulations that support their theoretical findings.
Fichier principal
Vignette du fichier
arxiv_vf.pdf (571.89 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04114338 , version 1 (02-06-2023)
hal-04114338 , version 2 (03-11-2023)


  • HAL Id : hal-04114338 , version 2


El Mehdi Saad, Gilles Blanchard, Nicolas Verzelen. Covariance Adaptive Best Arm Identification. NeurIPS 2023 - Neural Information Processing Systems, 2023, New Orleans, United States. ⟨hal-04114338v2⟩
79 View
37 Download


Gmail Mastodon Facebook X LinkedIn More