# Nonparametric Matrix Estimation with One-Sided Covariates

@article{Yu2021NonparametricME, title={Nonparametric Matrix Estimation with One-Sided Covariates}, author={Christina Lee Yu}, journal={ArXiv}, year={2021}, volume={abs/2110.13969} }

Consider the task of matrix estimation in which a dataset X ∈ Rn×m is observed with sparsity p, and we would like to estimate E[X], where E[Xui] = f(αu, βi) for some Holder smooth function f . We consider the setting where the row covariates α are unobserved yet the column covariates β are observed. We provide an algorithm and accompanying analysis which shows that our algorithm improves upon naively estimating each row separately when the number of rows is not too small. Furthermore when the…

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