Stochastic Optimization with Importance Sampling

@article{Zhao2014StochasticOW,
  title={Stochastic Optimization with Importance Sampling},
  author={Peilin Zhao and Tong Zhang},
  journal={CoRR},
  year={2014},
  volume={abs/1401.2753}
}
Uniform sampling of training data has been commonly used in traditional stochastic optimization algorithms such as Proximal Stochastic Gradient Descent (prox-SGD) and Proximal Stochastic Dual Coordinate Ascent (prox-SDCA). Although uniform sampling can guarantee that the sampled stochastic quantity is an unbiased estimate of the corresponding true quantity, the resulting estimator may have a rather high variance, which negatively affects the convergence of the underlying optimization procedure… CONTINUE READING
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