Research map / Learning theory and algorithms / Sample complexity
Instance-Optimal Private Density Estimation in the Wasserstein Distance
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Studies differentially private density estimation under Wasserstein distance and proposes an instance-optimal algorithm, based on the abstract's framing; the supplied full text is only the NeurIPS template, so details are unverifiable.
Problem
Differentially private density estimation of a distribution from samples, with error measured in Wasserstein distance (e.g., estimating population density over a geographic region).
Approach
Instance-optimal private density estimation in Wasserstein distance; the abstract is truncated and the provided text is only the NeurIPS template, so the specific technique cannot be confirmed from the text.
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