A Survey on Multi-player Bandits

Authors: Etienne Boursier, Vianney Perchet

JMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical A Survey on Multi-player Bandits Journal of Machine Learning Research 25 (2024) 1-45 ... This survey contextualizes and organizes the rich multiplayer bandits literature. In light of the existing works, some clear directions for future research appear.
Researcher Affiliation Collaboration Etienne Boursier EMAIL INRIA, Universit e Paris Saclay, LMO, Orsay, France Vianney Perchet EMAIL CREST, ENSAE Paris, Palaiseau, France CRITEO AI Lab, Paris, France
Pseudocode Yes Algorithm 1: Rand Orthogonalisation input: time T0, set S ηk(0) 1 for t [T0] do if ηk(t 1) = 1 then Sample k uniformly in S Pull arm k end
Open Source Code No The paper is a survey of existing literature and does not present new methodologies requiring source code. It does not contain any statements about making code available, nor does it provide links to code repositories.
Open Datasets No This paper is a survey of existing literature and does not present new experimental results requiring a dataset. It discusses applications and contexts where data might be used but does not provide access information for any dataset used in its own analysis.
Dataset Splits No This paper is a survey and does not perform experiments with datasets, therefore it does not contain information about training/test/validation dataset splits.
Hardware Specification No This paper is a survey and does not describe any experiments that would require specific hardware. Therefore, it does not contain hardware specifications.
Software Dependencies No This paper is a survey focusing on theoretical aspects and existing algorithms. It does not describe a software implementation of its own methodology, and therefore does not list software dependencies with version numbers.
Experiment Setup No This paper is a survey and does not present new experimental results. Consequently, it does not provide specific details about an experimental setup, hyperparameters, or training configurations.