SNAP：一种自洽一致原则及其在鲁棒计算中的应用
SNAP: A Self-Consistent Agreement Principle with Application to Robust Computation
AI总结：
SNAP是一种基于自洽一致原则的自监督框架，用于鲁棒计算，通过指数抑制异常值权重提升计算稳定性。
摘要：
我们介绍了SNAP（Self-coNsistent Agreement Principle），一种基于相互一致性的自监督框架，用于鲁棒计算。基于一致-可靠性假说，SNAP分配权重以量化一致程度，强调可信项并降低异常值的权重，无需监督或先验知识。关键结果是异常值权重的指数抑制，确保即使在高维设置中，异常值对计算的贡献可以忽略不计。我们研究了SNAP加权方案的性质，并展示了其在向量平均和子空间估计中的实际效益。特别是，我们证明非迭代SNAP在非迭代Weiszfeld算法和多变量中位数平均的两种变体中表现更优。SNAP因此提供了一种灵活、易用、广泛适用的鲁棒计算方法。
英文摘要：
We introduce SNAP (Self-coNsistent Agreement Principle), a self-supervised framework for robust computation based on mutual agreement. Based on an Agreement-Reliability Hypothesis SNAP assigns weights that quantify agreement, emphasizing trustworthy items and downweighting outliers without supervision or prior knowledge. A key result is the Exponential Suppression of Outlier Weights, ensuring that outliers contribute negligibly to computations, even in high-dimensional settings. We study properties of SNAP weighting scheme and show its practical benefits on vector averaging and subspace estimation. Particularly, we demonstrate that non-iterative SNAP outperforms the iterative Weiszfeld algorithm and two variants of multivariate median of means. SNAP thus provides a flexible, easy-to-use, broadly applicable approach to robust computation.
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