基于镜像下降法的鲁棒随机最优化算法
Algorithms of Robust Stochastic Optimization Based on Mirror Descent Method
摘要：
提出了一种基于随机梯度截断的凸复合随机最优化鲁棒非欧迭代算法的构造方法。对于这样的算法，我们建立亚高斯置信区间弱假设下的尾部的噪声分布在凸和强凸设置。一般的随机算法的准确性的鲁棒估计也提出了。
英文摘要：
We propose an approach to construction of robust non-Euclidean iterative algorithms for convex composite stochastic optimization based on truncation of stochastic gradients. For such algorithms, we establish sub-Gaussian confidence bounds under weak assumptions about the tails of the noise distribution in convex and strongly convex settings. Robust estimates of the accuracy of general stochastic algorithms are also proposed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
