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Importance weighting without importance weights: An efficient algorithm
  for combinatorial semi-bandits

Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits

17 March 2015
Gergely Neu
Gábor Bartók
ArXivPDFHTML

Papers citing "Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits"

7 / 7 papers shown
Title
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits
Mengmeng Li
Daniel Kuhn
Bahar Taşkesen
54
0
0
30 Sep 2024
Online Linear Optimization via Smoothing
Online Linear Optimization via Smoothing
Jacob D. Abernethy
Chansoo Lee
Abhinav Sinha
Ambuj Tewari
78
77
0
23 May 2014
An efficient algorithm for learning with semi-bandit feedback
An efficient algorithm for learning with semi-bandit feedback
Gergely Neu
Gábor Bartók
75
80
0
13 May 2013
Prediction by Random-Walk Perturbation
Prediction by Random-Walk Perturbation
Luc Devroye
Gábor Lugosi
Gergely Neu
79
37
0
23 Feb 2013
Towards minimax policies for online linear optimization with bandit
  feedback
Towards minimax policies for online linear optimization with bandit feedback
Sébastien Bubeck
Nicolò Cesa-Bianchi
Sham Kakade
OffRL
155
149
0
14 Feb 2012
Contextual Bandit Algorithms with Supervised Learning Guarantees
Contextual Bandit Algorithms with Supervised Learning Guarantees
A. Beygelzimer
John Langford
Lihong Li
L. Reyzin
Robert Schapire
OffRL
156
324
0
22 Feb 2010
The on-line shortest path problem under partial monitoring
The on-line shortest path problem under partial monitoring
Pál Benkö
T. Várady
L. Andor
Ralph Robert Martin
374
354
0
08 Apr 2007
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