The last decade has seen a number of advances in computationally efficient algorithms for statistical methods subject to robustness constraints. An estimator may be robust in a number of different ways: to contamination of the dataset, to heavy-tailed data, or in the sense that it preserves privacy of the dataset. We survey recent results in these areas with a focus on the problem of mean estimation, drawing technical and conceptual connections between the various forms of robustness, showing that the same underlying algorithmic ideas lead to computationally efficient estimators in all these settings.

翻译：过去十年中，针对受鲁棒性约束的统计方法，计算高效算法已取得诸多进展。估计器可通过多种不同方式实现鲁棒性：对数据集的污染具有鲁棒性、对重尾数据具有鲁棒性，或在保护数据集隐私的意义上具有鲁棒性。本文以均值估计问题为核心，综述这些领域的最新研究成果，通过建立不同鲁棒性形式之间的技术与概念关联，论证相同的底层算法思想可在所有这些场景中实现计算高效的估计器。