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Learning to Identify Top Elo Ratings: A Dueling Bandits Approach. (arXiv:2201.04480v1 [cs.LG])
Jan. 13, 2022, 2:10 a.m. | Xue Yan, Yali Du, Binxin Ru, Jun Wang, Haifeng Zhang, Xu Chen
cs.LG updates on arXiv.org arxiv.org
The Elo rating system is widely adopted to evaluate the skills of (chess)
game and sports players. Recently it has been also integrated into machine
learning algorithms in evaluating the performance of computerised AI agents.
However, an accurate estimation of the Elo rating (for the top players) often
requires many rounds of competitions, which can be expensive to carry out. In
this paper, to improve the sample efficiency of the Elo evaluation (for top
players), we propose an efficient online …
More from arxiv.org / cs.LG updates on arXiv.org
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