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Threshold Bandit Problem with Link Assumption between Pulls and Duels
Keshav Narayan · Aarti Singh

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in Complex feedback in online learning »

Building on prior work (Xu et al., 2020), we pro-pose a newer version of the Rank-Search algo-rithm, which aims to solve the Threshold BanditProblem when both duels and pulls are possible.Our algorithm, which requires an additional as-sumption connecting the borda scores of armswith their mean rewards, is able to perform in set-tings in which previous Rank-Search algorithmscannot. We conduct experiments comparing theperformance of the new algorithm with that ofolder versions of Rank-Search in various settings.Finally we prove upper bounds for the total num-ber of duels and pulls required by our proposedalgorithm, which we call Rank Search with Link(RS-L).

Author Information

Keshav Narayan (Carnegie Mellon University)
Keshav Narayan

I'm currently a Master's Student in Machine Learning at Carnegie Mellon University, and previously did my undergraduate degree in Computer Science at CMU as well. I'm joining DRW Cumberland as a Quantitative Trader in July, where I will be applying Machine Learning techniques to create algorithms for trading cryptocurrencies.

Aarti Singh (Carnegie Mellon University)

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