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Improving offline evaluation of contextual bandit algorithms via
  bootstrapping techniques

Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques

14 May 2014
Olivier Nicol
Jérémie Mary
Philippe Preux
    OffRL
ArXiv (abs)PDFHTML

Papers citing "Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques"

6 / 6 papers shown
Title
Bandit Algorithms for Precision Medicine
Bandit Algorithms for Precision Medicine
Yangyi Lu
Ziping Xu
Ambuj Tewari
109
14
0
10 Aug 2021
How and Why to Use Experimental Data to Evaluate Methods for
  Observational Causal Inference
How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference
A. Gentzel
Purva Pruthi
David D. Jensen
CML
67
18
0
06 Oct 2020
Productization Challenges of Contextual Multi-Armed Bandits
Productization Challenges of Contextual Multi-Armed Bandits
D. Abensur
Ivan Balashov
S. Bar
R. Lempel
Nurit Moscovici
I. Orlov
Danny Rosenstein
Ido Tamir
43
3
0
10 Jul 2019
Offline Evaluation of Ranking Policies with Click Models
Offline Evaluation of Ranking Policies with Click Models
Shuai Li
Yasin Abbasi-Yadkori
Branislav Kveton
S. Muthukrishnan
Vishwa Vinay
Zheng Wen
CMLOffRL
62
66
0
27 Apr 2018
The Use of Machine Learning Algorithms in Recommender Systems: A
  Systematic Review
The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review
I. Portugal
Paulo S. C. Alencar
Donald D. Cowan
149
615
0
17 Nov 2015
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
Adith Swaminathan
Thorsten Joachims
OffRL
156
167
0
09 Feb 2015
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