We consider the problem of accurately estimating the reliability of workers based on noisy labels they provide, which is a fundamental question in crowdsourcing. We propose a novel lower bound on the minimax estimation error which applies to any estimation procedure. We further propose Triangular Estimation (TE), an algorithm for estimating the reliability of workers. TE has low complexity, may...

We study nonparametric density estimation problems where error is measured in the Wasserstein distance, a metric on probability distributions popular many areas of statistics and machine learning. give first minimax-optimal rates for this problem general distances two classes densities: smooth densities [0,1]d bounded away from 0, sub-Gaussian lying Hölder class Cs, s∈(0,1). Unlike classical es...