Variance-Minimizing Augmentation Logging for Counterfactual Evaluation in Contextual Bandits

Author:

Tucker Aaron D.1ORCID,Joachims Thorsten1ORCID

Affiliation:

1. Cornell University, Ithaca, NY, USA

Funder

NSF

Publisher

ACM

Reference32 articles.

1. A. Agarwal , S. Basu , T. Schnabel , and T. Joachims . 2017. Effective Evaluation using Logged Bandit Feedback from Multiple Loggers . In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). A. Agarwal, S. Basu, T. Schnabel, and T. Joachims. 2017. Effective Evaluation using Logged Bandit Feedback from Multiple Loggers. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).

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 Proceedings of the 31st International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 32), Eric P. Xing and Tony Jebara (Eds.). PMLR, Bejing, China, 1638-- 1646 . http://proceedings.mlr.press/v32/agarwalb14.html 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 Proceedings of the 31st International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 32), Eric P. Xing and Tony Jebara (Eds.). PMLR, Bejing, China, 1638--1646. http://proceedings.mlr.press/v32/agarwalb14.html

3. Shipra Agrawal and Navin Goyal . 2013 . Thompson Sampling for Contextual Bandits with Linear Payoffs . In Proceedings of the 30th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 28), Sanjoy Dasgupta and David McAllester (Eds.). PMLR, Atlanta, Georgia, USA, 127-- 135 . http://proceedings.mlr.press/v28/agrawal13.html Shipra Agrawal and Navin Goyal. 2013. Thompson Sampling for Contextual Bandits with Linear Payoffs. In Proceedings of the 30th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 28), Sanjoy Dasgupta and David McAllester (Eds.). PMLR, Atlanta, Georgia, USA, 127--135. http://proceedings.mlr.press/v28/agrawal13.html

4. Alina Beygelzimer and John Langford. 2009. The offset tree for learning with partial labels. In KDD. ACM 129--138. Alina Beygelzimer and John Langford. 2009. The offset tree for learning with partial labels. In KDD. ACM 129--138.

5. Alberto Bietti , Alekh Agarwal , and John Langford . 2018. A contextual bandit bake-off. arXiv preprint arXiv:1802.04064 ( 2018 ). Alberto Bietti, Alekh Agarwal, and John Langford. 2018. A contextual bandit bake-off. arXiv preprint arXiv:1802.04064 (2018).

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