Upper-Confidence-Bound Algorithms for Active Learning in Mulit-armed Bandits
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Standard
Upper-Confidence-Bound Algorithms for Active Learning in Mulit-armed Bandits. / Auer, Peter; Carpentier, Alexandra; Lazaric, Alessandro et al.
The 22nd International Conference on Algorithmic Learning Theory. 2011. p. 189-203.
The 22nd International Conference on Algorithmic Learning Theory. 2011. p. 189-203.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Harvard
Auer, P, Carpentier, A, Lazaric, A, Ghavamzadeh, M & Munos, R 2011, Upper-Confidence-Bound Algorithms for Active Learning in Mulit-armed Bandits. in The 22nd International Conference on Algorithmic Learning Theory. pp. 189-203.
APA
Auer, P., Carpentier, A., Lazaric, A., Ghavamzadeh, M., & Munos, R. (2011). Upper-Confidence-Bound Algorithms for Active Learning in Mulit-armed Bandits. In The 22nd International Conference on Algorithmic Learning Theory (pp. 189-203)
Vancouver
Auer P, Carpentier A, Lazaric A, Ghavamzadeh M, Munos R. Upper-Confidence-Bound Algorithms for Active Learning in Mulit-armed Bandits. In The 22nd International Conference on Algorithmic Learning Theory. 2011. p. 189-203
Author
Bibtex - Download
@inproceedings{032ca9f943144ae59373a9c3c57ee752,
title = "Upper-Confidence-Bound Algorithms for Active Learning in Mulit-armed Bandits",
author = "Peter Auer and Alexandra Carpentier and Alessandro Lazaric and Mohammad Ghavamzadeh and R{\`e}mi Munos",
year = "2011",
language = "English",
pages = "189--203",
booktitle = "The 22nd International Conference on Algorithmic Learning Theory",
}
RIS (suitable for import to EndNote) - Download
TY - GEN
T1 - Upper-Confidence-Bound Algorithms for Active Learning in Mulit-armed Bandits
AU - Auer, Peter
AU - Carpentier, Alexandra
AU - Lazaric, Alessandro
AU - Ghavamzadeh, Mohammad
AU - Munos, Rèmi
PY - 2011
Y1 - 2011
M3 - Conference contribution
SP - 189
EP - 203
BT - The 22nd International Conference on Algorithmic Learning Theory
ER -
