Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical Study. Anderson, C. & Phillips, J. M. Transactions on Machine Learning Research, February, 2025. abstract bibtex Robust statistics aims to compute quantities to represent data where a fraction of it may be arbitrarily corrupted. The most essential statistic is the mean, and in recent years, there has been a flurry of theoretical advancement for e!ciently estimating the mean in high dimensions on corrupted data. While several algorithms have been proposed that achieve near-optimal error, they all rely on large data size requirements as a function of dimension. In this paper, we perform an extensive experimentation over various mean estimation techniques where data size might not meet this requirement due to the highdimensional setting.
@article{anderson_robust_2025,
title = {Robust {High}-{Dimensional} {Mean} {Estimation} {With} {Low} {Data} {Size}, an {Empirical} {Study}},
volume = {02},
abstract = {Robust statistics aims to compute quantities to represent data where a fraction of it may be arbitrarily corrupted. The most essential statistic is the mean, and in recent years, there has been a flurry of theoretical advancement for e!ciently estimating the mean in high dimensions on corrupted data. While several algorithms have been proposed that achieve near-optimal error, they all rely on large data size requirements as a function of dimension. In this paper, we perform an extensive experimentation over various mean estimation techniques where data size might not meet this requirement due to the highdimensional setting.},
language = {en},
journal = {Transactions on Machine Learning Research},
author = {Anderson, Cullen and Phillips, Jeff M.},
month = feb,
year = {2025},
keywords = {Observable},
}
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