Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider the problem of prediction with expert advice when the losses of the experts have low-dimensional structure: they are restricted to an unknown $d$-dimensional subspace.  devise algorithms with regret bounds that are independent of the number of experts and depend only on the rank $d$. For the stochastic model  show a tight bound of $\Theta(\sqrt{dT})$, and extend it to a setting of an approximate $d$ subspace. For the adversarial model  show an upper bound of $O(d\sqrt{T})$ and a lor bound of $\Omega(\sqrt{dT})$.
['Elad Hazan', 'Tomer Koren', 'Roi Livni', 'Yishay Mansour']
I am writing a research paper on a idea Online Learning with Low Rank Experts
[{"from": "human", "value": "I am writing a research paper on a idea Online Learning with Low Rank Experts"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider the problem of prediction with expert advice when the losses of the experts have low-dimensional structure: they are restricted to an unknown $d$-dimensional subspace.  devise algorithms with regret bounds that are independent of the number of experts and depend only on the rank $d$. For the stochastic model  show a tight bound of $\\Theta(\\sqrt{dT})$, and extend it to a setting of an approximate $d$ subspace. For the adversarial model  show an upper bound of $O(d\\sqrt{T})$ and a lor bound of $\\Omega(\\sqrt{dT})$."}]