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pro vyhledávání: '"Martin, Jamie"'
Autor:
Chennu, Srivas, Maher, Andrew, Pangerl, Christian, Prabanantham, Subash, Bae, Jae Hyeon, Martin, Jamie, Goswami, Bud
AB testing aids business operators with their decision making, and is considered the gold standard method for learning from data to improve digital user experiences. However, there is usually a gap between the requirements of practitioners, and the c
Externí odkaz:
http://arxiv.org/abs/2307.14628
Real-world applications of reinforcement learning for recommendation and experimentation faces a practical challenge: the relative reward of different bandit arms can evolve over the lifetime of the learning agent. To deal with these non-stationary c
Externí odkaz:
http://arxiv.org/abs/2206.13960
Stochastic delays in feedback lead to unstable sequential learning using multi-armed bandits. Recently, empirical Bayesian shrinkage has been shown to improve reward estimation in bandit learning. Here, we propose a novel adaptation to shrinkage that
Externí odkaz:
http://arxiv.org/abs/2106.11294
Autor:
Martin, Jamie
This research is submitted to the Faculty of Humanities, University of the Witwatersrand, Johannesburg in partial fulfilment of the requirements for the degree of Master of Arts in the field of Diversity Studies.
This study examines the discours
This study examines the discours
Externí odkaz:
https://hdl.handle.net/10539/26337
Autor:
Duval, Benjamin D., Curtsinger, Heather D., Hands, Aubrey, Martin, Jamie, McLaren, Jennie R., Cadol, Daniel D.
Publikováno v:
Plant Ecology, 2020 Mar 01. 221(3), 177-189.
Externí odkaz:
https://www.jstor.org/stable/48740264
Autor:
Duval, Benjamin D.1 (AUTHOR) benjamin.duval@nmt.edu, Martin, Jamie1 (AUTHOR), Marsalis, Mark A.2 (AUTHOR)
Publikováno v:
Agronomy. Dec2022, Vol. 12 Issue 12, p3109. 17p.
Akademický článek
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