We consider stochastic convex optimization problems, where several machines
act asynchronously in parallel while sharing a common memory. We propose a
robust training method for the constrained setting and derive non asymptotic
convergence guarantees that do not depend on prior knowledge of update delays,
objective smoothness, and gradient variance. Conversely, existing methods for
this setting crucially rely on this prior knowledge, which render them
unsuitable for essentially all shared-resources computational environments,
such as clouds and data centers. Concretely, existing approaches are unable to
accommodate changes in the delays which result from dynamic allocation of the
machines, while our method implicitly adapts to such changes.

针对多台异步运行的机器共同访问的内存环境下的随机凸优化问题，我们提出了一种鲁棒的约束训练方法，其非渐近收敛保证不依赖于更新延迟、目标平滑度和梯度方差的先验知识。与此相反，现有方法严重依赖于这些先验知识，因此不适用于所有共享资源的计算环境，如云和数据中心。与现有方法不同，我们的方法可以隐含地适应动态分配机器所带来的延迟变化。