Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a family of non-uniform sampling strategies to provably speed up a class of stochastic optimization algorithms with linear convergence including Stochastic Variance Reduced Gradient (SVRG) and Stochastic Dual Coordinate Ascent (SDCA). For a large family of penalized empirical risk minimization problems, our methods exploit data dependent local smoothness of the loss functions near the optimum, while maintaining convergence guarantees. Our bounds are the first to quantify the advantage gained from local smoothness which are significant for some problems significantly better. Empirically,  provide thorough numerical results to back up our theory. Additionally  present algorithms exploiting local smoothness in more aggressive ways, which perform even better in practice.
['Daniel Vainsencher', 'Han Liu', 'Tong Zhang']
I am writing a research paper on a idea Local smoothness in Variance Reduced optimization
[{"from": "human", "value": "I am writing a research paper on a idea Local smoothness in Variance Reduced optimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a family of non-uniform sampling strategies to provably speed up a class of stochastic optimization algorithms with linear convergence including Stochastic Variance Reduced Gradient (SVRG) and Stochastic Dual Coordinate Ascent (SDCA). For a large family of penalized empirical risk minimization problems, our methods exploit data dependent local smoothness of the loss functions near the optimum, while maintaining convergence guarantees. Our bounds are the first to quantify the advantage gained from local smoothness which are significant for some problems significantly better. Empirically,  provide thorough numerical results to back up our theory. Additionally  present algorithms exploiting local smoothness in more aggressive ways, which perform even better in practice."}]