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内部出版物
CNKI学位论文(321)
知网期刊论文(163)
在“
Elsevier电子期刊
”中,
命中:
63
条,耗时:0.0249907 秒
在所有数据库中总计命中:
484
条
1.
Two “little treasure games” driven by unconditional
regret
关键词:
D8
;
C7
;
D7
刊名:Economics Letters
出版年:2017
2.
Modification of improved upper confidence
bound
s for regulating exploration in Monte-Carlo tree search
作者:
Yun-Ching Liu
;
cipherman@logos.t.u-tokyo.ac.jp" class="auth_mail" title="E-mail the corresponding author
;
Yoshimasa Tsuruoka
;
tsuruoka@logos.t.u-tokyo.ac.jp" class="auth_mail" title="E-mail the corresponding author
关键词:
Monte-Carlo tree search
;
Multi-armed bandit
;
Upper confidence
bound
s
刊名:Theoretical Computer Science
出版年:2016
3.
The robust (minmax
regret
) single machine scheduling with interval processing times and total weighted completion time objective
作者:
Jordi Pereira
a
;
b
;
jorge.pereira@uai.cl" class="auth_mail" title="E-mail the corresponding author
;
jorge.pereira@upc.edu" class="auth_mail" title="E-mail the corresponding author
关键词:
Scheduling
;
Single machine
;
Uncertainty
;
Robust optimization
;
Branch-and-
bound
刊名:Computers and Operations Research
出版年:2016
4.
Complexity of interval minmax
regret
scheduling on parallel identical machines with total completion time criterion
作者:
Maciej Drwal
a
;
maciej.drwal@pwr.edu.pl" class="auth_mail" title="E-mail the corresponding author
;
Roman Rischke
b
;
rischke@ma.tum.de" class="auth_mail" title="E-mail the corresponding author
关键词:
Robust optimization
;
Scheduling
;
Uncertainty
;
Computational complexity
刊名:Operations Research Letters
出版年:2016
5.
An improved upper
bound
on the expected
regret
of UCB-type policies for a matching-selection bandit problem
作者:
Ryo Watanabe
ryo@main.ist.hokudai.ac.jp" class="auth_mail" title="E-mail the corresponding author
;
Atsuyoshi Nakamura
;
atsu@main.ist.hokudai.ac.jp" class="auth_mail" title="E-mail the corresponding author
;
Mineichi Kudo
mine@main.ist.hokudai.ac.jp" class="auth_mail" title="E-mail the corresponding author
关键词:
Multi-armed bandit problem
;
Matching
;
Regret
analysis
;
Combinatorial bandit
;
Online learning
刊名:Operations Research Letters
出版年:2015
6.
Dealing with uncertainty in the smart grid: A learning game approach
作者:
Hé
;
lè
;
ne Le Cadre
;
a
;
helene.le-cadre@ensta-paristech.fr" class="auth_mail" title="E-mail the corresponding author
;
helene.lecadre@gmail.com" class="auth_mail" title="E-mail the corresponding author
Author Vitae
;
Jean-Sé
;
bastien Bedo
b
;
jeansebastien.bedo@orange.com" class="auth_mail" title="E-mail the corresponding author
Author Vitae
关键词:
Learning
;
Regret
;
Optimization under uncertainty
;
Game theory
刊名:Computer Networks
出版年:2016
7.
Bandit online optimization over the permutahedron
作者:
Nir Ailon
a
;
nailon@cs.technion.ac.il" class="auth_mail" title="E-mail the corresponding author
;
Kohei Hatano
b
;
hatano@inf.kyushu-u.ac.jp" class="auth_mail" title="E-mail the corresponding author
;
Eiji Takimoto
b
;
eiji@inf.kyushu-u.ac.jp" class="auth_mail" title="E-mail the corresponding author
关键词:
Combinatorial online prediction
;
Bandit
;
Permutation
;
Permutahedron
刊名:Theoretical Computer Science
出版年:2016
8.
Robust option pricing: Hannan and Blackwell meet Black and Scholes
作者:
Peter M. DeMarzo
a
;
pdemarzo@stanford.edu" class="auth_mail" title="E-mail the corresponding author
;
Ilan Kremer
b
;
c
;
Yishay Mansour
d
;
e
关键词:
C73
;
D81
;
G13
刊名:Journal of Economic Theory
出版年:2016
9.
Multi-armed bandit problem with known trend
作者:
Djallel Bouneffouf
a
;
dbouneffouf@bcgsc.ca" class="auth_mail" title="E-mail the corresponding author
Author Vitae
;
Raphael Fé
;
raud
b
;
Raphael.feraud@orange.com" class="auth_mail" title="E-mail the corresponding author
Author Vitae
关键词:
Multi-armed bandit
;
Online learning
;
Recommender systems
刊名:Neurocomputing
出版年:2016
10.
Efficient crowdsourcing of unknown experts using
bound
ed multi-armed bandits
作者:
Long Tran-Thanh
;
ltt08r@ecs.soton.ac.uk" class="auth_mail
;
Sebastian Stein
ss2@ecs.soton.ac.uk" class="auth_mail
;
Alex Rogers
acr@ecs.soton.ac.uk" class="auth_mail
;
Nicholas R. Jennings
nrj@ecs.soton.ac.uk" class="auth_mail
关键词:
Crowdsourcing
;
Machine learning
;
Multi-armed bandits
;
Budget limitation
刊名:Artificial Intelligence
出版年:September 2014
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