v1v2v3 (latest)

Learning the distribution with largest mean: two bandit frameworks

31 January 2017

E. Kaufmann

Aurélien Garivier

ArXiv (abs)PDF HTML

Papers citing "Learning the distribution with largest mean: two bandit frameworks"

8 / 8 papers shown

Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits

Zixin Zhong

Wang Chi Cheung

Vincent Y. F. Tan

291

16 Oct 2021

Bandits with many optimal armsNeural Information Processing Systems (NeurIPS), 2021

R. D. Heide

J. Cheshire

Pierre Ménard

Alexandra Carpentier

102

23 Mar 2021

$Stochastic Bandits with Vector Losses: Minimizing $\ell^\infty$-Norm of Relative Losses$

Stochastic Bandits with Vector Losses: Minimizing

\ell^\infty

-Norm of Relative Losses

Xuedong Shang

Han Shao

Jian Qian

141

15 Oct 2020

Hyper-Parameter Optimization: A Review of Algorithms and Applications

Tong Yu

Hong Zhu

AAML

237

630

12 Mar 2020

Adaptive Sampling for Estimating Multiple Probability Distributions

S. Shekhar

T. Javidi

Mohammad Ghavamzadeh

208

28 Oct 2019

Fixed-Confidence Guarantees for Bayesian Best-Arm IdentificationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019

234

24 Oct 2019

The Finite-Horizon Two-Armed Bandit Problem with Binary Responses: A Multidisciplinary Survey of the History, State of the Art, and Myths

P. Jacko

161

20 Jun 2019

Pure Exploration in Infinitely-Armed Bandit Models with Fixed-ConfidenceInternational Conference on Algorithmic Learning Theory (ALT), 2018

128

13 Mar 2018