Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  discusses how to efficiently choose from n unknown distributions the k ones whose means are the greatest by a certain metric, up to a small relative error.  study the topic under two standard settings—multi-armed bandits and hidden bipartite graphs—which differ in the nature of the input distributions. In the former setting, each distribution can be sampled (in the i.i.d. manner) an arbitrary number of times, whereas in the latter, each distribution is defined on a population of a finite size m (and hence, is fully revealed after m samples). For both settings,  prove lor bounds on the total number of samples needed, and propose optimal algorithms whose sample complexities match those lor bounds.
['Wei Cao', 'Jian Li', 'Yufei Tao', 'Zhize Li']
I am writing a research paper on a idea On Top-k selection in multi-armed bandits and hidden bipartite graphs
[{"from": "human", "value": "I am writing a research paper on a idea On Top-k selection in multi-armed bandits and hidden bipartite graphs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  discusses how to efficiently choose from n unknown distributions the k ones whose means are the greatest by a certain metric, up to a small relative error.  study the topic under two standard settings\u2014multi-armed bandits and hidden bipartite graphs\u2014which differ in the nature of the input distributions. In the former setting, each distribution can be sampled (in the i.i.d. manner) an arbitrary number of times, whereas in the latter, each distribution is defined on a population of a finite size m (and hence, is fully revealed after m samples). For both settings,  prove lor bounds on the total number of samples needed, and propose optimal algorithms whose sample complexities match those lor bounds."}]