In this paper, we propose a Double Thompson Sampling (D-TS) algorithm for dueling bandit problems. As its name suggests, D-TS selects both the first and the second candidates according to Thompson Sampling. Specifically, D-TS maintains a posterior distribution for the preference matrix, and chooses the pair of arms for comparison according to two sets of samples independently drawn from the pos...

We describe the results of Copeland surface replacement shoulder arthroplasty using the mark III prosthesis in patients over 80 years of age. End-stage arthritis of the shoulder is a source of significant pain and debilitating functional loss in the elderly. An arthroplasty offers good relief of pain and may allow the patient to maintain independence. The risk-benefit ratio of shoulder replacem...

Professor Charlton Copeland offers a wide-ranging, ambitious critique of what he characterizes as federalism jurisprudence’s dominant models of “separation” and “allocation” of authority between the respective federal and state spheres. Judicial resolution of federalism questions, he suggests, turns inappropriately and incompletely on the moment of a law’s enactment. This “obsession with the le...

in this research priority of gorgan’s sub- watershed were done after normalizing the indicators using gis software. the multi-attribute decision-making methods including electre, topsis and saw were used in this study. ahp was applied in order to weighting the indicators in expert choice software. first, priority the sub-watershed were done by electre, topsis and saw using the excel. each metho...

The rank aggregation problem has received significant recent attention within the computer science community. Its applications today range far beyond the original aim of building metasearch engines to problems in machine learning, recommendation systems and more. Several algorithms have been proposed for these problems, and in many cases approximation guarantees have been proven for them. Howev...

We study theK-armed dueling bandit problem, a variation of the standard stochastic bandit problem where the feedback is limited to relative comparisons of a pair of arms. The hardness of recommending Copeland winners, the arms that beat the greatest number of other arms, is characterized by deriving an asymptotic regret bound. We propose Copeland Winners Relative Minimum Empirical Divergence (C...

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