Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study an online learning framework introduced by Mannor and Shamir (2011) in which the feedback is specified by a graph, in a setting where the graph may vary from round to round and is never fully revealed to the learner.  show a large gap beten the adversarial and the stochastic cases. In the adversarial case,  prove that even for dense feedback graphs, the learner cannot improve upon a trivial regret bound obtained by ignoring any additional feedback besides her own loss. In contrast, in the stochastic case  give an algorithm that achieves e ( p T ) regret over T rounds, provided that the independence numbers of the hidden feedback graphs are at most .  also extend our results to a more general feedback model, in which the learner does not necessarily observe her own loss, and show that, even in simple cases, concealing the feedback graphs might render a learnable problem unlearnable.
['Alon Cohen', 'Tamir Hazan', 'Tomer Koren']
I am writing a research paper on a idea Online Learning with Feedback Graphs Without the Graphs
[{"from": "human", "value": "I am writing a research paper on a idea Online Learning with Feedback Graphs Without the Graphs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study an online learning framework introduced by Mannor and Shamir (2011) in which the feedback is specified by a graph, in a setting where the graph may vary from round to round and is never fully revealed to the learner.  show a large gap beten the adversarial and the stochastic cases. In the adversarial case,  prove that even for dense feedback graphs, the learner cannot improve upon a trivial regret bound obtained by ignoring any additional feedback besides her own loss. In contrast, in the stochastic case  give an algorithm that achieves e ( p T ) regret over T rounds, provided that the independence numbers of the hidden feedback graphs are at most .  also extend our results to a more general feedback model, in which the learner does not necessarily observe her own loss, and show that, even in simple cases, concealing the feedback graphs might render a learnable problem unlearnable."}]