Variance Reduction and Low Sample Complexity in Stochastic Optimization via Proximal Point Method
<|reference_start|>Variance Reduction and Low Sample Complexity in Stochastic Optimization via Proximal Point Method: This paper proposes a stochastic proximal point method to solve a stochastic convex composite optimization problem. High probability results in stochastic optimization typically hinge on restrictive assumptions on the stochastic gradient noise, for example, sub-Gaussian distributions. Assuming only weak conditions such as bounded variance of the stochastic gradient, this paper establishes a low sample complexity to obtain a high probability guarantee on the convergence of the proposed method. Additionally, a notable aspect of this work is the development of a subroutine to solve the proximal subproblem, which also serves as a novel technique for variance reduction.<|reference_end|>
@article{liang2024variance,
    title={Variance Reduction and Low Sample Complexity in Stochastic Optimization
  via Proximal Point Method},
    author={Jiaming Liang},
    journal={arXiv preprint arXiv:2402.08992},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08992},
    primaryClass={math.OC cs.LG stat.ML}
}