QUE-scoring via quantum entropy regularization achieves optimal robust mean estimation in Õ(nd) time and outperforms prior outlier detection methods in experiments.
Sub-gaussian estimators of the mean of a random vector
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
Two new DP median estimators achieve sub-Gaussian deviations for unbounded variables without moments; DP mean estimators under heavy tails show strictly worse deviations than non-private versions.
citing papers explorer
-
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
QUE-scoring via quantum entropy regularization achieves optimal robust mean estimation in Õ(nd) time and outperforms prior outlier detection methods in experiments.
-
Differentially private sub-Gaussian location estimators
Two new DP median estimators achieve sub-Gaussian deviations for unbounded variables without moments; DP mean estimators under heavy tails show strictly worse deviations than non-private versions.