Long-Term Value of Exploration: Measurements, Findings and Algorithms

Author:

Su Yi1ORCID,Wang Xiangyu2ORCID,Le Elaine Ya2ORCID,Liu Liang2ORCID,Li Yuening2ORCID,Lu Haokai1ORCID,Lipshitz Benjamin2ORCID,Badam Sriraj2ORCID,Heldt Lukasz2ORCID,Bi Shuchao2ORCID,Chi Ed H.1ORCID,Goodrow Cristos2ORCID,Wu Su-Lin2ORCID,Baugher Lexi2ORCID,Chen Minmin1ORCID

Affiliation:

1. Google Deepmind, Mountain View, USA

2. Google, Mountain View, USA

Publisher

ACM

Reference48 articles.

1. Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári. 2011. Improved algorithms for linear stochastic bandits. Advances in neural information processing systems 24 (2011).

2. Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, and Robert Schapire. 2014. Taming the monster: A fast and simple algorithm for contextual bandits. In International Conference on Machine Learning. PMLR, 1638--1646.

3. Shipra Agrawal and Navin Goyal. 2013. Thompson sampling for contextual bandits with linear payoffs. In International conference on machine learning. PMLR, 127--135.

4. ExcUseMe

5. Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. 2002. Finite-time analysis of the multiarmed bandit problem. Machine learning 47, 2 (2002), 235--256.

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