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Omri Ben-Eliezer

2 accepted papers

2022

Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions

NeurIPS 2022accept

The last few years have seen a surge of work on high dimensional statistics under privacy constraints, mostly following two main lines of work: the "worst case" line, which does not make any distributional assumptions on the input data; and the "strong assumptions" line, which assumes that the data…

Cited by 7SourcePDFScholar