We study optimization algorithms based on variance reduction for stochastic
gradient descent (SGD). Remarkable recent progress has been made in this
direction through development of algorithms like SAG, SVRG, SAGA. These
algorithms have been shown to outperform SGD, both theoretically and
empirically. However, asynchronous versions of these algorithms---a crucial
requirement for modern large-scale applications---have not been studied. We
bridge this gap by presenting a unifying framework for many variance reduction
techniques. Subsequently, we propose an asynchronous algorithm grounded in our
framework, and prove its fast convergence. An important consequence of our
general approach is that it yields asynchronous versions of variance reduction
algorithms such as SVRG and SAGA as a byproduct. Our method achieves near
linear speedup in sparse settings common to machine learning. We demonstrate
the empirical performance of our method through a concrete realization of
asynchronous SVRG.

该研究探讨了基于方差缩减的优化算法，尤其是异步版本的 SVRG 和 SAGA 在机器学习中的应用和实验表现。研究结果表明，该方法在稀疏设置下实现了近线性加速。