论文标题
强大的估计算法不需要知道腐败水平
Robust estimation algorithms don't need to know the corruption level
论文作者
论文摘要
真实数据很少纯净。因此，过去半个世纪以来，即使部分数据损坏，这些算法也非常感兴趣。但是，只有在损坏数据的比例紧密的上限时，他们的绝大多数方法只有最佳的准确性。这样的范围在实践中不可用，从而导致保证弱，而且性能往往差。此简短说明将复杂而普遍的鲁棒性问题提取为简单的几何难题。然后，它应用了难题的解决方案来得出一种通用的元技术，该元技术将转换任何可靠的估计算法，需要紧密的腐败级上限以实现其最佳精度，以实现基本相同的精度而无需使用任何上限。
Real data are rarely pure. Hence the past half-century has seen great interest in robust estimation algorithms that perform well even when part of the data is corrupt. However, their vast majority approach optimal accuracy only when given a tight upper bound on the fraction of corrupt data. Such bounds are not available in practice, resulting in weak guarantees and often poor performance. This brief note abstracts the complex and pervasive robustness problem into a simple geometric puzzle. It then applies the puzzle's solution to derive a universal meta technique that converts any robust estimation algorithm requiring a tight corruption-level upper bound to achieve its optimal accuracy into one achieving essentially the same accuracy without using any upper bounds.
