TY - GEN
T1 - Anytime optimal algorithms in stochastic multi-armed bandits
AU - Degenne, Rémy
AU - Perchet, Vianney
N1 - Publisher Copyright:
© 2016 by the author(s).
PY - 2016/1/1
Y1 - 2016/1/1
N2 - We introduce an anytime algorithm for stochastic multi-armed bandit with optimal distribution free and distribution dependent bounds (for a specific family of parameters). The performances of this algorithm (as well as another one motivated by the conjectured optimal bound) are evaluated empirically. A similar analysis is provided with full information, to serve as a benchmark.
AB - We introduce an anytime algorithm for stochastic multi-armed bandit with optimal distribution free and distribution dependent bounds (for a specific family of parameters). The performances of this algorithm (as well as another one motivated by the conjectured optimal bound) are evaluated empirically. A similar analysis is provided with full information, to serve as a benchmark.
UR - https://www.scopus.com/pages/publications/84998630604
M3 - Conference contribution
AN - SCOPUS:84998630604
T3 - 33rd International Conference on Machine Learning, ICML 2016
SP - 2391
EP - 2409
BT - 33rd International Conference on Machine Learning, ICML 2016
A2 - Weinberger, Kilian Q.
A2 - Balcan, Maria Florina
PB - International Machine Learning Society (IMLS)
T2 - 33rd International Conference on Machine Learning, ICML 2016
Y2 - 19 June 2016 through 24 June 2016
ER -