The minimization of convex functions which are only available through partial
and noisy information is a key methodological problem in many disciplines. In
this paper we consider convex optimization with noisy zero-th order
information, that is noisy function evaluations at any desired point. We focus
on problems with high degrees of smoothness, such as logistic regression. We
show that as opposed to gradient-based algorithms, high-order smoothness may be
used to improve estimation rates, with a precise dependence of our upper-bounds
on the degree of smoothness. In particular, we show that for infinitely
differentiable functions, we recover the same dependence on sample size as
gradient-based algorithms, with an extra dimension-dependent factor. This is
done for both convex and strongly-convex functions, with finite horizon and
anytime algorithms. Finally, we also recover similar results in the online
optimization setting.

本文探讨了利用仅包含部分和嘈杂信息的凸函数最小化问题，特别着重于高度平滑问题的凸优化问题，包括逻辑回归等。研究表明，相对于基于梯度的算法，高阶平滑性可用于改善估计速率，并精确地依赖于平滑度的程度，同时提出了此类算法可行性的上限。最后，作者还在在线优化设置中取得了类似的结果。